Demand for data science professionals is growing, and educational institutions such as the Nanyang Technology University (NTU) have started offering interdisciplinary offerings that incorporate data science with topics such as economics.

But can good data scientists be churned out by simply putting them through a curriculum or having them sit through some certification?

In a contributed opinion piece to The Business Times, David Hardoon, a senior advisor in data and artificial intelligence for UnionBank argues that the diversification and complexity of data science pose a real challenge to data science education.

Data science is a complex field

“[Data science] is a hybrid of statistics, machine learning, and data mining, and covers programming languages, technology frameworks, development platforms, as well as visualization tools,” writes Hardoon.

“How does one balance the multiplicity of areas, covering them with sufficient depth in order to produce a well-rounded data scientist who can provide impactful value to a future employer?”

Indeed, in an article earlier this year titled “Placing ‘Practice’ at the Center of Data Science Education”, Eric Kolaczyk, Haviland Wright, and Masanao Yajima described the downsides of typical postsecondary training approaches in core data science fields.

Practice is treated as something that is done only to capstone projects or as a finale, they wrote, and this results in “students, upon exiting academia, needing a nontrivial ramping-up period before they can truly have an impact with their first employers.”

Having helmed or served in advisor roles to a diverse range of organizations in Singapore such as the Corrupt Investigation Practices Bureau (CPIB), the Central Provident Fund (CPF), and the Monetary Authority of Singapore (MAS), Hardoon notes that this feedback is common and goes beyond the degree itself and into the practical applications of the coursework.

“The majority of data scientists, upon completion of their education, still require a hands-on education at their place of employment to refine their learned skills to practical necessities… a freshly minted, well-rounded data scientist is unlikely to hit the ground running at their first employ despite a presumption otherwise,” he wrote.

A call for change

To help data scientists close this gap and produce effective work quickly upon completion of training, organizations might want to consider operationalizing data science by identifying key data science roles within the organization, suggests Hardoon.

One solution is to segregate between key components of data science operations, with the creation of roles. Some examples: Data science developer (Focus on data science methodologies and techniques), data science engineer (Focus on data piping, data quality, and data ingestion), data science solution architect (Deep understanding of platforms and data enterprise architecture), and data science storyteller (Focus on data visualization with strong business acumen).

Alternatively, existing data science programs can be tailored to offer greater depth with a focus on niche areas or professional specialization to better reflect the maturity of the data science field today, Hardoon notes.

Don’t expect this to happen tomorrow, though. While we do see data science moving in the direction suggested by Hardoon, these changes will take time yet to fully materialize.

In the meantime, employers may just have to accept that a newly minted data scientist is unlikely to produce valuable, actionable insights from day one – or even the first few months. To help a new data science hire get started quicker, additional on-the-ground training is essential.

The post When a Data Science Education is Not Enough appeared first on Artificial Intelligence.

Data science courses are high in demand. A data scientist needs to interpret the data accurately because he/she is responsible to communicate the complexity of the topic to the target audience in easiest ways.

Data science courses are very high in demand in the recent times. At least 88% of the Data Scientists hold Master’s degree and 46% are PhDs. While there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.

To become a data scientist, you can opt for a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data.

According to a research by LinkedIn, the supply of workers with the correct skills isn’t sufficient, thus making data science skills among the most in-demand. Thankfully, there are plenty of online learning opportunities available today to help become a highly-skilled data scientist.

Below are a few of the most popular data science options:

IntelliPaat’s Data scientist Master’s program in collaboration with IBM: This training program offered in collaboration with IBM lets learners gain proficiency in Data Science with Python, Machine Learning, AI, Deep Learning, Big Data Hadoop, Spark, Tableau Desktop, etc with 24*7 support. This program comprises 10+ courses, 53+ industry-specific projects, and 232 Hours of online instructor-led training with a CAPSTONE project. Learners will get access to the IBM course and earn a certificate from IBM.

The best part of this course is that learners have lifetime access to attend multiple batches and keep themselves abreast with technology updates. This course also provides career assistance which includes resume preparation, mock interviews.

GreatLearning Data Scientist program in collaboration with Great Lakes: Designed for freshers and young professionals exploring rewarding careers in Data Science. With Python, SQL, Tableau, Data Science and Machine Learning tools & techniques and its applications. They teach through immersive lectures delivered by our expert faculty in a classroom. Great Lakes faculty have been ranked among India’s Top Data Science academicians.

UpGrad Data Scientist Masters program in collaboration with Liverpool John Moores University: Any specialization can be chosen regardless of background. The program caters to: Engineers, Marketing & Sales Professionals, Freshers, Data Professionals, Domain Experts, Software & IT Professionals. They teach Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, etc

Simplilearn’s Data Scientist Master’s Program in Collaboration with IBM: This course offered in joint collaboration introduces students to integrated blended learning, equipping them with expertise in both Artificial Intelligence and Data Science. This advanced course in Data Science will make students industry-ready for skills in Artificial Intelligence and Data Science and consequently render them job-ready. This Data Science certification training provides hands-on exposure to key technologies including R, Python, Machine Learning, Tableau, Hadoop, and Spark via live interaction with practitioners, practical labs, and industry projects.

 
Coursera Data Scientist Program in collaboration with Johns Hopkins University: In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

The post Want to become a Data Scientist? Best course to pursue in India appeared first on Artificial Intelligence.

While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data.

Udacity, a silicon-valley-based global lifelong learning platform, has released the list of the most popular Nanodegree programs in India in 2020. The data is based on the number of enrollments during the year, showing the demand across different states and union territories.

Deep learning and data engineering are top Nanodegree programs showing the country’s growing interest in AI and data. While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data.

There is no doubt about the fact that COVID-19 has changed the global job landscape. This can be clearly seen in how the demand for some of the Nanodegree programs shifted in 2020 vis-a-vis 2019. Take a look:

There has always been huge traction amongst learners to upskill themselves on the tech front. In the wake of the pandemic, rapid digital adoption has further made advanced courses more desirable worldwide for forward-looking professionals. The findings reinstate that the demand for technology-oriented jobs is continually growing and many regions have also been able to leverage technology to upskill themselves for futuristic job profiles.

Here are some key findings:

Deep learning is big in Karnataka

Karnataka holds the lion’s share for maximum Nanodegree programs. As much as 24% demand for Deep Learning and 34% of the total demand for Data Engineering Nanodegree programs comes from this state.  The demand for AI Product Manager (38%) and Product Manager (60%) is also the highest in the state. 

Maharashtra digs on data

Data Science and Deep Learning are the most popular Nanodegree programs in Maharashtra. The Product Manager Nanodegree program is popular in Maharashtra. More than 40% of the enrollments come from this state. It is also a popular destination for data science Nanodegree program and Machine Learning Engineer Nanodegree program with 25% and 21% enrollments coming from these regions respectively.

Delhi is all about data & programming

The Indian capital, Delhi, is a frontrunner in the mainstream programming languages. It drew 22% and 23% demand for C++ and Full Stack Web Developer. The Data Analyst Nanodegree program is also big in the region with 21% enrollments coming from the National Capital Region.

Telangana produces more developers

The state of Telangana racked in 16% of enrollments for the Full Stack Web Development Nanodegree program and C++ Nanodegree programs. The Machine Learning Nanodegree program is also big in the state with around 11% enrollments coming from it.

More than half of self-driving car engineers come from Tamil Nadu

Tamil Nadu produced more than 50% Self-Driving car engineers in 2020 in India. Computer Vision and Machine Learning Nanodegree programs are also popular in the state.

The post Deep Learning & Data Engineering Are Top In-demand Skills Of 2020 In India: Reports appeared first on Artificial Intelligence.

The shift towards deep learning and data engineering is an indication of the country’s growing interest in artificial intelligence and big data

Udacity has released the list of the most popular Nanodegree programs in India in 2020. The data is based on the number of enrollments during the year, showing the demand across different states and union territories. Deep learning and data engineering were the top Nanodegree programs showing the country’s growing interest towards artificial intelligence and data. While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data.

There is no doubt about the fact that COVID-19 has changed the global job landscape, said a statement from the company. This can be clearly seen in how the demand for some of the Nanodegree programs shifted in 2020 vis-a-vis 2019. Take a look:

There has always been huge traction amongst learners to upskill themselves on the tech front. In the wake of the pandemic, rapid digital adoption has further made advanced courses more desirable worldwide for forward-looking professionals. The findings reinstate that the demand for technology-oriented jobs is continually growing and many regions have also been able to leverage technology to upskill themselves for futuristic job profiles.

Here are some key findings with regard to deep learning

1. Deep Learning is Big in Karnataka

Karnataka holds the lion’s share for maximum Nanodegree programs. As much as 24% demand for Deep Learning and 34% of the total demand for Data Engineering Nanodegree programs comes from this state.  The demand for AI Product Manager (38%) and Product Manager (60%) is also the highest in the state.

2. Maharashtra Digs On Data

Data Science and Deep Learning are the most popular Nanodegree programs in Maharashtra. The Product Manager Nanodegree program is popular in Maharashtra. More than 40% of the enrollments come from this state. It is also a popular destination for data science Nanodegree program and Machine Learning Engineer Nanodegree program with 25% and 21% enrollments coming from these regions respectively.

3. Delhi is All About Data and Programming

The Indian capital, Delhi, is a frontrunner in the mainstream programming languages. It  drew 22% and 23% demand for C++ and Full Stack Web Developer. The Data Analyst Nanodegree program is also big in the region with 21% enrollments coming from the National Capital Region.

4. Telangana Produces More Developers

The state of Telangana racked in 16% of enrollments for the Full Stack Web Development Nanodegree program and C++ Nanodegree programs. The Machine Learning Nanodegree program is also big in the state with around 11% enrollments coming from it.

5. More than half Self-Driving Car Engineers Come From Tamil Nadu

Tamil Nadu produced more than 50% Self-Driving car engineers in 2020 in India. Computer Vision and Machine Learning Nanodegree programs are also popular in the state.

The post Deep Learning was Top In-demand Skill of 2020, List of Most Popular Nanodegree Programs appeared first on Artificial Intelligence.

In the world of big data, the demand for highly skilled data scientists has never been higher. But the competition for employment within this specialized field of IT is also fierce. That means that data science certification is essential if you aim to develop your career and land one of the top jobs.

In this article, we’ll take a look at the field of data science before then looking at your options for gaining data science certification while being based in India.

What is Data Science?

Historically, the focus has been on effective methods of capturing and then storing large volumes of data. But with effective technology now in place for both of these processes, there are growing demands to be able to analyze the information. 

Data science utilizes a range of tools, algorithms, and machine learning strategies to identify patterns within the raw data.  However, it isn’t just about looking to what’s already happened; the data scientist uses their results to make decisions and predictions as to future events. To do this, three fundamental approaches are used:

1. Predictive Causal Analytics

Being able to predict the likelihood of future events is incredibly important in a range of business sectors. This is where predictive casual analytics come into play to reduce risk factors. If, for example, you run a subscription-based service and you wanted to be able to model the likelihood of clients continuing their subscription payments over a period of time, then you would need to utilize predictive casual analytics to be able to make a judgment for this taking place.

2. Prescriptive Analytics   

This relatively new field within data science develops a model to make decisions and modify dynamic parameters. Applying prescriptive analytics means that the client is provided with predictions and has suggestions for actions to be taken and their associated outcomes.

A hospital is one setting in which prescriptive analytics can make a significant difference in ensuring the most effective use of funds. Analyzing patient data can provide insights into the number of staff needed on shift, the demands on diagnostic equipment, and the volume of medical supplies that will be required.

3. Machine Learning.

There are two aspects of machine learning: firstly, making predictions and then completing pattern discovery. Predictions requires the data to already be available, which will then be used to train the machines. On the other hand, pattern discovery is used in situations where the parameters to make the predictions are not available. This then means that hidden patterns within the dataset need to be identified to then make meaningful predictions.  

What To Look For in a Data Science Certification Course

When you’re committing both your time and money to your education, the course you choose must deliver results. Now while the effort and time you put in will have a significant impact on the likelihood of you achieving certification, you also need to ensure the course provides the following:

World-Class Faculty

Do you know who will be teaching? Do they have relevant and recent industry experience, or are they merely regurgitating information from a textbook? This is an industry that is going through rapid change; it sees new innovations and methodology on what seems to be a daily basis. That means that it’s essential that the faculty are teaching industry best practices.

Delivery Methods

Sometimes it’s just not possible to relocate to another part of India to take a course. Family commitments, funding, travel restrictions can all cause significant problems to the traditional classroom style of learning. So, that means that it’s essential that your data science certification provider can offer a range of delivery methods.  

Online learning has become an accepted way to study and even more so since the Covid-19 pandemic. Ensuring that your provider can offer the right method of delivery for you means that your studies need not be interrupted no matter what else is going on in the world.

Faculty: Student Ratio

When you’re one of many, it can prove challenging to get the individual feedback you need to progress. Does your lecturer know who you are? Do they know your aspirations, the challenges you face, and the support you need? That’s why it’s essential to ask the provider what their faculty: student ratio is. If it’s much over 1:10, then it’s unlikely that you’re going to get the personalized attention you need to be successful.

Program Curriculum

Will you get the opportunity to focus on the topics which are of most interest to you? The ones which are most relevant to gain entry to the sector of your choice? While all courses will have core modules that everyone is required to take, you should also check whether there is an opportunity for electives. These might be in specialized areas such as data mining, robotics, or deep learning applications. Having that knowledge in a niche field of data science could be the key to launching your career in data science.

Application of Knowledge

It’s one thing to be able to discuss techniques, but it’s a whole other thing to be able to effectively apply them to real-life scenarios. Your data science course should have a strong emphasis on the application of your newly developed skillset.

So, you need to look for courses that include activities such as boot camps or hackathons. In these sessions, you’ll have the opportunity to gain hands-on experience, develop your critical thinking abilities and work within a team, all while being up against strict deadlines. After all, they’re precisely the skills that your new employer will want to see.

Next Steps in Achieving Your Data Science Certification

If you’re sure that this is the right career direction for you, then the next step is to begin looking for providers who can offer opportunities for candidates based within India. This may sound straightforward, but every year students find themselves on courses that are just not a great match for their needs.

Perhaps the course doesn’t have collaborations with universities from across the world, limiting opportunities for future study. Or maybe the certification lacks recognition or standing within the industry. The time you spend now on researching courses and finding the right one for you is an essential investment in the success of your career within data science.

The post How To Get Your Data Science Certification In India. appeared first on Artificial Intelligence.

There is a growing need for statisticians and data analysts nationwide, as computational advancements have afforded researchers the ability to generate and analyze mass quantities of data. To meet the demand, employment opportunities for statisticians are expected to grow by 33 percent within the decade.

Southern Illinois University Edwardsville’s Center for Predictive Analytics is leading a statewide fellowship program funded by the U.S. Department of Agriculture’s National Institute of Food and Agriculture through its Research and Extension Experiences for Undergraduates program.

Under the direction of C-PAN Director Carolyn Butts-Wilmsmeyer, the project, Preparing Undergraduates for New Frontiers in Data Analysis: Experiential Learning in Applied Statistics (ELIAS) Fellows, is concurrently training undergraduate students in real-world data analysis and hands-on research in a greenhouse, laboratory or field setting.

Institutional collaborators include the University of Illinois at Urbana-Champaign, Illinois State University, Northeastern Illinois University and Parkland Community College. 

“The overall goal of the ELIAS fellowship program is to produce students who can meet the need for an increasingly data-driven workforce, particularly in the life sciences,” Butts-Wilmsmeyer explained. “Upon graduation, students in statistics and data science are placed in multidisciplinary teams consisting of chemists, biologists and business personnel.

“However, current training in data science and statistics often does not occur in laboratory, greenhouse, field or other applied research settings, making it difficult for graduates to understand the limitations in these research environments and to communicate findings across disciplinary bounds. Through this program, students are placed in a two-year, dually immersive research experience in applied statistics/data science and a laboratory, greenhouse or field research environment, based on the students’ interests.”

Fellow Sam Garcia, an environmental science major at NEIU, is in her second year of the program.

“I have been passionate about environmental science since I learned about climate change in elementary school,” she said. “I particularly became interested in data-driven research because quantifying and analyzing data brings order and significance to the information that can be found through science.”

Under the mentorship of Geddes, and in collaboration with Urban Rivers, Garcia is analyzing the effects of artificial floating wetlands on macroinvertebrate communities in the Chicago River.

“The ELIAS program has provided me the opportunity to carry out my own independent research project, which is preparing me for graduate school,” Garcia said. “After graduate school, I plan to pursue a research career in the marine or atmospheric science field at NOAA, NASA or a similar organization. My long-term intentions are to use science as a tool to incite change that will help preserve the environment.”

Butts-Wilmsmeyer notes the USDA’s recognition of the need for programs that support the recruitment and training of traditionally underrepresented groups in the food and agricultural sciences. As such, the ELIAS Fellows’ recruitment efforts emphasize women and minorities, as well as transfer students from community colleges.

“While the fellowship program is open to all students in the agricultural and life sciences, and all fellows will be provided with unfailing support, we recognize that there may be some hurdles which female students, transfer students and underrepresented minorities may face at a higher frequency than their classmates,” Butts-Wilmsmeyer explained. “Our mentor team actively works with all of our fellows to identify ways to overcome any hurdles they may face during the completion of their degree and progression toward their desired careers.”

Participating students receive full funding for their research, as well as a $7,250 stipend each year of the fellowship. They will present their findings at UIUC Agronomy Day, symposia at their respective institutions and at a scientific conference of their choosing.

The post Fellowship aims to meet demand for data science professionals appeared first on Artificial Intelligence.

After graduating at the University of Pennsylvania in 2012, Chloe Ho (MBA 2019) began her post-undergrad career in New York working for Morgan Stanley. While there, she served in a strategy and analytics role, working on projects for sales and trading management. However, after four years on the job, she realized that there was something missing—she knew that she needed more to give her career an edge.

“I could sense an increasing appetite for data and data-driven managerial decision-making,” says Ho. “I felt that this was a trend that was occurring across all industries, beyond just my immediate one of financial services. This highlighted the need for me to gain data fluency and competency—to invest in building out my data and analytics skillset for the sake of my career.”

When Ho decided to explore different career options for similar strategy roles outside the finance sector, the teams at tech-savvy startups, such as Squarespace, quickly confirmed her data-minded suspicion.

“I would successfully pass through several rounds of interviews, only to be stymied later on when handed a laptop for live coding exercises,” she says.

With these experiences in mind, Ho took matters into her own hands to break the technological glass ceiling.

She decided to invest in external courses—specifically, the Data Science program at General Assembly—to bolster her data and analytics expertise. During her 10-week course, Ho learned the Python coding language, picked up best practices in data mining and exploratory analysis, and familiarized herself with the ins and outs of various machine learning models.

Ho took this newly acquired data and analytics knowledge with her to HBS, and, subsequently, landed an internship at the Bay Area–based Samsung Innovation and Strategy Center during summer 2018.

While she appreciated the resources of a large organization like Samsung, Ho wanted to be closer to the action and build her experience in big data and artificial intelligence at a smaller firm. She accepted a job as Manager, Business Operations and Strategy, at Scale AI, joining the summer after she graduates from HBS. This Bay Area firm specializes in labeling and annotating data, creating high-quality datasets to inform use cases from autonomous vehicles to drones. Scale’s technology accelerates AI development by democratizing access to intelligent data.

“It has been said that ‘data is the new oil’—it is the next key and critical resource, one that confers significant competitive advantage to organizations and companies,” says Ho. “However, it is also generated and collected from all of us, and the insights derived feed into a wide variety of use cases, from recommendations on Netflix to personalized healthcare.”

As for kicking off her new career post HBS, Ho is excited to be a part of the booming AI revolution that provides not only innumerable consumer benefits, competitive advantage, and efficiency gains from the data being collected, but one that also presents larger existential implications.

“As this field continues to involve, there are broader implications on data ownership and governance, notions of personal privacy and ethics, and on the evolution of man-machine interactions and interfaces,” says Ho. “We as a society have the opportunity to decide where we stand on these issues and to proactively put in place the policies and institutions to enable a future that is not only data-driven, but one that is also just, ethical, and fair.”

The post CHLOE HO, MBA 2019: DATA-DRIVEN AND IN DEMAND appeared first on Artificial Intelligence.

The bright future of the planet appears to be in grip of Artificial Intelligence (AI) technology. From industry giants like Amazon and Google to even small startups in India are driving digital evolution with adoption of AI into their businesses. Along with the companies, even their leaders have adapted to the vision focusing on AI-centric growth that can operate on algorithms and hence enable better customer experience.

The appeal of the technology is so at the peak that AI career profiles are becoming more and more famous in the market. Emerging trends of AI along with its subset machine learning has made it the most remarkable career in tech-industry.

Here we have enlisted top 10 job profiles, along with their hottest vacancy, seeking artificial intelligence and machine learning skills.

1. Amazon (New Delhi)- Business Development Manager

Role Description:

Professionals with broad and deep experience in both the business and technical aspects of cloud computing, especially in artificial intelligence (AI) and machine learning (ML) are suitable for the post of AI Business Development Manager. Having rich experience understanding and influencing compute technology choices and building programs to remove obstacles to adoption with public sector customers (governments, educational institutions, non-profits) can add up to the resume well. The Business Development Manager in general, helps business leaders and technologists adopt new cloud services and envision a future where smarter machines solve complex business problems.

Responsibilities:

•  Build a strong understanding of AI/ML technology and related industry developments, including certain companies’ services.

•  Communicate internally and externally about company’s AI/ML approach, value proposition, and offerings. Help field teams understand AI/ML services and help customers successfully adopt them.

•  Understand and uncover customers’ challenges in adopting AI/ML, and show how company solutions can help them provide new services, new capabilities, and new efficiencies.

•  Work with partners, internal teams, and customers to determine best use-cases, deployment, and adoption of AI/ML and related technologies, etc.

•  Work closely with global account teams of the company and sales and technical leadership to build programs that accelerate adoption of AI/ML, including generating technical content and white papers.

•  Define and deploy AI/ML campaigns and work with sales and field teams to implement them.

•  Identify and remove obstacles that block adoption of AI/ML and related workloads.

Qualification & Eligibility:

•  10 years or more of relevant technical leadership, product management, business development, in and around information technologies.

•  3 years or more of experience in AI/ML (e.g., product management, business development, technical marketing, etc.).

•  1 or more years of experience with public cloud computing services.

•  Bachelor’s degree in computer science, engineering or related technical degree.

•  In-depth experience in AI/ML technology, market dynamics, and customer requirements.

•  Demonstrated ability to think strategically about business, product, and technical challenges.

•  Demonstrated ability to build and scale programs that remove blockers to customer adoption and accelerate adoption.

•  Strong organizational, presentation, and writing skills.

Technical and Other Knowledge:

The ideal candidate for the job will possess technical expertise in AI and ML, along with a diverse technical, IT management, and/or engineering background. The person need to have knowledge of cloud computing, industry trends, and product developments as well.

The role of business development manager requires a strong passion for working with customers and partners, a high level of comfort navigating ambiguity, and attention to detail. Candidate will need to travel for face-to-face customer engagements, internal conferences, and industry events.

Salary Range:

Average Salary Ranges From 5 to 7 lac per annum.

2. Accenture (Bengaluru)-AI Team Leader

Role Description:

The person will be a critical member of an engineering team working on architecture, developing and augmenting cutting edge AI technology, which is a crucial part of our next generation AI based financial service platform and leading a team of R&D Engineers.

He/she should be master of almost everything including Artificial Intelligence, Machine Learning/Natural Language Processing (practical or/& computational linguistics)/ Deep Learning (text/vision/speech)/Reinforcement Learning (practical applications).

Responsibilities:

The selected candidate will be responsible for the entire development cycle of the solution, turning an idea into a working product, including, definition, implementation, testing and debugging. Optionally also, discussion with partners and strategic customers. He/she should be able to lead a team while adapting to AI technologies with consumer-centric approach.

Qualification & Eligibility:

•  At least 3-years hands-on experience with machine learning and neural networks and overall 5-8 years of experience in AI centric profile.

•  Good theoretical understanding of CNN RNN, supervised learning, reinforcement learning.

•  Ability to take a task from concept to full implementation.

•  Creative thinking.

•  Experience in data collection in simulation and field.

•  Experience in DNN framework infrastructure (TensorFlow Caffe etc).

•  Ability to lead team and interact with other teams.

•  MATLAB/python as mother language.

 Technical and Other Knowledge:

•  Familiar with Java / J2EE, Python.

•  Familiar with Apache OpenNLP or NLTK, ClearTK etc.

•  Expertise with NLP algorithms like Entity Recognition and Extraction, Text Classification, Query Parsers.

•  Understands Linguistics and the underlying algorithms behind NLP.

•  Familiar with NLP in an Internationalised Context.

•  Familiarity with AWS, Linux, Unix.

•  Familiarity with Git, SVN, Jira.

•  Familiarity with Agile Tools and Methodologies.

Salary Range:

Average Salary Ranges From 6 to 8 lac per annum.

3. Amazon (Bengaluru)-Data Scientist

Role Description:

As a Data Scientist, the candidate will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques. The person will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

The candidate should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of high-impact business questions and present the insights in concise and effective manner. Additionally, the candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.

Responsibilities:

•  Use predictive analytics and machine learning techniques to solve complex problems and drive business decisions.

•  Employ the appropriate algorithms to discover patterns of risks, abuse and help reduce bad debt.

•  Design experiments, test hypotheses, and build actionable models to optimize company operations.

•  Solve analytical problems, and effectively communicate methodologies and results.

•  Build predict models to forecast risks for product launches and operations and help predict workflow and capacity requirements for company operations.

•  Draw inferences and conclusions, and create dashboards and visualizations of processed data, identify trends, anomalies.

•  Work closely with internal stakeholders such as business teams, engineering teams, and partner teams and align them with respect to your focus area.

 Qualification & Eligibility:

•  Bachelor’s Degree in any quantitative discipline such as Statistics, Mathematics, Quantitative Finance or Operational Research.

•  At least 3 years of experience working in Analytics / Business Intelligence environment.

•  Experience in working with databases and SQL in a business environment.

•  Prior experience in design and execution of analytical projects.

•  Worked extensively in large scale databases and data warehouses.

•  Experience in e-commerce / on-line companies in fraud / risk control functions.

•  Experience in statistical techniques such as classification, clustering, regression, statistical inference, collaborative filtering, and natural language processing, experimental design, social networking analysis, feature engineering, etc.

•  Compelling communication and influencing skills and participation in winning the support of management and influence the course of major strategic decisions.

 Technical and Other Knowledge:

•  Demonstrated use of analytical packages and query languages such as SAS, SPSS and SQL.

•  Experience/knowledge of advanced machine learning techniques such as GBM, random forest, etc.

•  Coding skills in one of the modern languages Java, Python, Scala, R.

•  Experience with visualization technologies such as Tableau.

 Salary Range:

Average Salary Ranges From 7 to 10 lac per annum.

4. Wipro (Bengaluru)-ML Architect

Role Description:

The candidate will be entitled to build scalable machine learning systems and data-driven products working with cross functional teams Working w/ cloud services like AWS, Microsoft, IBM, and Google Cloud Working w/ one or more of the following Natural Language Processing, text understanding, classification, pattern recognition, recommendation systems, targeting systems, ranking systems or similar.

Responsibilities:

•   Research, design, develop, and modify computer vision and machine learning algorithms and models.

•   leveraging experience with technologies such as Caffe, Torch, or TensorFlow.

•   Shape product strategy for highly contextualized applied ML/AI solutions by engaging with customers, solution teams, discovery workshops and prototyping initiatives.

•   Help build a high-impact ML/AI team by supporting recruitment, training and development of team members.

•   Serve as evangelist by engaging in the broader ML/AI community through research, speaking/teaching, formal collaborations and/or other channels.

•   Designing integrations of and tuning machine learning & computer vision algorithms.

•   Research and prototype techniques and algorithms for object detection and recognition Convolutional neural networks (CNN) for performing image classification and object detection.

Qualification & Eligibility:

Have experience 2-5 years of work or educational experience in Machine Learning or Artificial Intelligence Creation and application of Machine Learning algorithms to a variety of real-world problems with large datasets.

Have Contribution to research communities and/or efforts, including publishing papers at conferences such as NIPS, ICML, ACL, CVPR, etc.

Technical and Other Knowledge:

•   Familiarity with Embedded Vision Processing Systems Open source tools & platforms Statistical Modelling, Data Extraction, Analysis, Construct, train, evaluate and tune neural networks

•   Java

•   C++

•   Python

•   Deep Learning frameworks such as Caffe OR Torch OR TensorFlow

•   Image/video vision library like OpenCV, Clarafai, Google Cloud Vision etc

•   Supervised & Unsupervised Learning

•   Developed feature learning, text mining, and prediction models (eg, deep learning, collaborative filtering, SVM, and random forest) on big data computation platform (Hadoop, Spark, HIVE, and Tableau)

•   Tableau, Hadoop, Spark, HBase, Kafka

Salary Range:

Average Salary Ranges From 4 to 6 lac per annum.

5. Google (Hyderabad)-Machine Learning Engineer

Role Description:

The person will help the company create artificial intelligence products. He/she will be entitled to create machine learning models and retraining systems. To do this job successfully, the person needs exceptional skills in statistics and programming. Having knowledge of data science and software engineering can probably get him the desired job.

•   The ultimate goal of ML Engineer will be to shape and build efficient self-learning applications.

Responsibilities:

•   Designing and developing machine learning and deep learning systems.

•   Running machine learning tests and experiments.

•   Implementing appropriate ML algorithms.

•   Study and transform data science prototypes.

•   Design machine learning systems.

•   Research and implement appropriate ML algorithms and tools.

•   Develop machine learning applications according to requirements.

•   Select appropriate datasets and data representation methods.

•   Run machine learning tests and experiments.

•    Perform statistical analysis and fine-tuning using test results.

•   Train and retrain systems when necessary.

•   Extend existing ML libraries and frameworks.

•   Keep abreast of developments in the field.

Qualification & Eligibility:

•   Qualification and relevant work experience in same field is required.

•   Proven experience as a Machine Learning Engineer or similar role.

•   Ability to work in a team.

•   Outstanding analytical and problem-solving skills.

•   BSc in Computer Science, Mathematics or similar field.

•   Master’s degree is a plus.

Technical and Other Knowledge:

•   Understanding of data structures, data modeling and software architecture.

•   Deep knowledge of math, probability, statistics and algorithms.

•   Ability to write robust code in Python, Java and R.

•   Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).

•   Excellent communication skills.

Salary Range:

Average Salary Ranges From 4 to 7 lac per annum.

6. Microsoft (Hyderabad)-AI Engineer

Role Description:

The person will be responsible for commercialization of Artificial Intelligence, Cognitive Computing and Machine Intelligence Technologies for company’s businesses. Currently, companies are looking for well knowledge and experienced engineer who is keen to involve in new initiative of AI-nization projects across companies.

AI Engineer is expected to be familiar with wider scope of knowledge and experiences on designing and implementing of AI applications by applying available cognitive computing APIs and machine learning technologies from both internal and external companies.

Responsibilities:

•   AI Engineer is responsible forunderstanding the business problems.

•   Identifying/applying right AI or cognitive computing technologies to solve problems and involving in formulation and execution of technologies recipes for commercial deployment.

•   Understand the business problem, challenge of existing technologies and areas of application for AI technologies.

•   Identify and choose right AI or cognitive computing technologies for solving problems and formulate AI recipes for development.

•   Develop required machine learning models or prototype applications applying formulated AI recipes and verify the problem/solution fit

•   Involve in development of AI Platform and AI-nization projects.

 Qualification & Eligibility:

•   Minimum 3 years of development experience using one of JAVA, C, C++ or python languages.

•   Minimum 3 years of experience in web application development.

•   Minimum 2 years of experience in building of API driven interface development.

•   Experience in developing cloud applications running on Microsoft Azure, Amazon Web Service, Google Cloud, IBM Bluemix etc.

•   Experience in designing and implementing systems using emerging technology.

•   Experience in agile software development or similar methodologies.

•   Software prototyping.

•   Ability to learn new and emerging technologies.

•   Excellent verbal and written communication skills.

•   Demonstrated ability to deliver working solutions on a tight schedule.

•   Strong analytical thinking and problem-solving skills.

 Technical and Other Knowledge:

•   Artificial Intelligence, Cognitive Computing, APIs, Machine Learning, Deep Learning Technologies.

•   Semantic Web, Knowledge Engineering, Natural Language Processing.

•   Experiences with RDBM databases, NoSQL Database, Graph Database.

•   Experience in search platform such as Solr, Elastic Search.

•   Voice Recognition/Synthesis.

Salary Range:

Average Salary Ranges From 7 to 13 lac per annum

7. Amazon (Delhi)-ML Specialist

Role Description:

Machine Learning Specialist is a professional specialized in developing Machine learning, which can “learn” from or adapt to the data and make predictions.

The candidate should be bright & passionate about Machine Learning. He/she should be a team player who strives for engineering and technical excellence and enjoys in bringing innovation in a fast-paced environment.

The ML Specialist position offers excellent development prospects, the ability to develop skills to the highest level and be compensated with a competitive salary and fantastic benefits package.

Responsibilities:

•   Design and develop machine learning algorithms.

•   Discover, design, and develop analytical methods to support novel approaches of data and information processing.

•   Design and develop novel algorithms.

•   Perform explanatory data analyses.

•   Generate and test working hypotheses.

•   Prepare and analyze historical data and identify patterns.

•   Provide technical support for program management and business development activities including proposal writing and customer development.

•   Own and manage knowledge sharing within a community.

Qualification & Eligibility:

•   Education and Experience PhD in computer science, engineering, or related field with focus on Machine Learning.

•   MSc with machine learning focus plus 5+ years professional work experience.

Technical and Other Knowledge:

•   Experience with Bayesian optimization, Distributed learning algorithms, Predictive modeling.

•   Core Skills Excellent C++ development skills on Linux platforms (C++, Java, Python).

•   Experience developing software as part of a team.

Salary Range:

Average Salary Ranges From 12 to 15 lac per annum.

8. Bosch Group (Bengaluru)-ML Intelligent System Researcher

Role Description:

The companies with vacancy for ML Intelligent System Researcher are looking for creative & motivated researchers to work on topics related to Pervasive Computing/Intelligent Systems. The candidate is expected to have the ability to explore real life problems and identify the research challenges as part of the team engaged in bringing Intelligence to the next generation of Intelligent Systems using Machine Learning. The candidate will work as part of a talented team, supported by a software engineering team. He/she will be among the first to see and use this technology to solve real problems in ways never attempted by others.

The ideal candidate should have the ability to perform foundation research, to take initiative, to work in international teams and the necessary development/implementation skills to accomplish the proof of concept.

Responsibilities:

•   Research and develop innovative ML strategies for challenging problems that will affect future products.

•   Collaborate with broader teams across company to transfer AI/ML solutions.

•   Grab opportunities to provide technical mentorship and guidance, prepare technical reports for publication and conference talks.

•   Be self-motivated, excellent problem solver, and a fast learner.

•   Quick to forge relationships and comfortable with change and ambiguity.

•   Strong programming skills with tried experience crafting, prototyping, and delivering advanced algorithmic solutions.

Qualification & Eligibility:

•   PhD. from reputed Indian institutes (IISc, IITs, IIITs, etc.) or from reputed international institutes with good track record of publications in the areas of Computer Science/ Electronics and Communications Engineering/ Electrical and related branches with background in Machine Learning and application of Machine Learning to develop Intelligent Systems.

•   PhD. could be any of the areas of:

• Good knowledge on any of the below topics:

• • • Machine Learning for Intelligent Systems
    • Pervasive Computing
    • Context Aware Systems
    • IoT Data Analytics
  • Familiarity with programming languages: Python/R/Java/
    • Statistical Learning Theory
    • Time Series Analysis
    • Probabilistic Graphical Models
    • Reinforcement Learning
  • Ability to design experiment and create proof-of-concept deployments
  • Excellent verbal and written communication skills
  • Excellent presentation and mass communication skills
  • Entrepreneurial skills/experience

 Salary Range:

Average Salary Can Vary From 4 to 24 lac per annum (Depending On Location And Work Experience).

9. Adobe (Delhi NCR – Greater Noida)-ML Applied Research Scientist

Role Description:

The companies are looking for an expert in machine learning to help them extract value from their data. The ideal candidate will lead all the processes from data collection, cleaning, and pre-processing, to training models and deploying them to production.

The person will be passionate about machine learning and stay up-to-date with the latest developments in the field.

Responsibilities:

•   Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.

•   Managing available resources such as hardware, data, and personnel so that deadlines are met.

•   Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.

•   Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.

•   Verifying data quality, and/or ensuring it via data cleaning.

•   Supervising the data acquisition process if more data is needed.

•   Finding available datasets online that could be used for training.

•   Defining validation strategies.

•   Defining the pre-processing or feature engineering to be done on a given dataset.

•   Defining data augmentation pipelines.

•   Training models and tuning their hyper-parameters.

•   Analyzing the errors of the model and designing strategies to overcome them.

•   Deploying models to production.

Qualification & Eligibility:

•   PhD degree in Machine Learning / Data Science / Statistics or Masters with 4+ years of experience in the aforementioned fields.

•   Deep knowledge of fundamentals of AI, Machine / Deep Learning, Data Mining and Predictive Modelling is required with solid experience in applying these techniques on real world problems.

•   Interdisciplinary skills in Big Data Technologies, ETL, statistics and causal inference is desirable.

•   Hands on experience in solution building, deployment, testing, and release processes using version control and continuous integration is desirable.

Technical and Other Knowledge:

•   Strong skills in software engineering practices (Design, Development and Requirement Management) with expertise in applicable programming languages and frameworks such as scikit-learn, XGBoost, Pytorch, Spacy, H2O.

•   Strong analytical, written and verbal communication skills.

•   Demonstrated ability to propose novel solutions to problems, performing experiments to prove feasibility of solutions and working to refine the solutions into real world context.

•   Proficiency with a deep learning framework such as TensorFlow or Keras.

•   Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas.

•   Expertise in visualizing and manipulating big datasets.

•   Proficiency with OpenCV.

•   Familiarity with Linux.

•   Ability to select hardware to run an ML model with the required latency.

Salary Range:

Average Salary Can Vary From 12 to 14 lac per annum.

 10. JP Morgan Chase & Co. (Hyderabad)-Data Scientist – Vice President

Role Description:

As Vice President and Data Scientist of the company, the candidate will be responsible for creating the overall AI roadmap for the company and build a cohesive and comprehensive AI strategy for execution of the plan as well as provide technical guidance and align company objectives with development plans. The person will champion AI advancement and lead major strategic and cross-functional initiatives to drive implementation of the roadmap. He/she will oversee AI related research and development and work with multiple teams to develop and implement innovative algorithms and architectures.

Responsibilities:

•   Work with the other executive of the team to create an AI roadmap and strategy that aligns with company business goals.

•   Provide technical leadership and expertise for research and development of AI technology for our autonomous driving solution.

•   Help guide the team design, develop, implement, and evaluate both traditional as well as deep learning solutions.

•   Manage and lead a cross functional team of diverse and creative individuals and build a culture of innovation and execution.

•   Work on building efficient workflow for experimenting, developing, and testing AI algorithms and architecture including data collection and annotation.

•   Focus on developing technology that is high performing and suitable for implementation in a real-world production environment and meets the product and business requirements.

•   Keep abreast of latest development of AI and ML ideas and algorithms both from academic and industry.

 Qualification & Eligibility:

•   PHD in computer science or related fields with AI/ML specialization.

•  Proven track record of 12+ years of progressive data science and ML/AI experience, with 3+ years in a significant leadership position managing large tech teams that designed, developed, and launched complex AI solutions.

•   Previous experience autonomous driving projects, especially in perception, predictions and motion planning, is preferred.

•   Demonstrated track record of successfully hiring, motivating, and retaining talented engineers.

•   Knowledge of machine learning, information retrieval, analytics, statistics, deep learning techniques.

 Technical and Other Knowledge:

•   Hands on experience in deep learning platform (such as Tensorflow) and models (such as DNN, CNN, RNN, GAN).

•   Experience and experience with GPU and CPU based efficient and scalable compute platform for deep learning.

•   Experience delivering cloud-based solutions at scale.

•   Understanding of security and compliance constraints is a plus.

•   Experience in development of multi-threaded event driven serving systems in C++/Python.

•   Competent in fundamental data structures, algorithms, and computer system.

•   Strong verbal and written communication skills.

The post THE 10 MOST IN-DEMAND AI JOB OPPORTUNITIES IN INDIA appeared first on Artificial Intelligence.