AI and data science jobs are already seen as a rewarding career path for professionals

Artificial intelligence is a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold of artificial intelligence is no joke. Along with artificial intelligence, data science has also shifted the way we live and work. With the demand for data science and artificial intelligence spiralling, the job market is opening its door to AI and data science jobs. The tech sphere has ensured that artificial intelligence jobs and data science jobs provide limitless opportunities for professionals to explore cutting edge solutions. According to a Gartner report, Artificial intelligence jobs rose to over 2.3 million in 2020. While the competition in the industry is heating up, AI and Data science jobs are already seen as the rewarding career path. Analytics Insight has listed top AI and Data science jobs that aspirants should apply for today.

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Data Scientist: Artificial Intelligence at IBM–

Location: Bengaluru, Karnataka, India

About the company: IBM, also known as International Business Machines Corporation is a leading American computer manufacturer. The company has developed a thoughtful, comprehensive approach to corporate citizenship that they believe aligns with IBM’s values and maximized the impact they can make as a global enterprise.

Roles and responsibilities: As a data scientist at IBM, the candidate is expected to develop, maintain, and evaluate AI solutions. He/she will be involved in the design of data solutions using artificial intelligence-based technologies like H2O, Tensorflow. They are responsible for designing algorithms and implementation including loading from disparate datasets, pre-processing using Hive and Pig. The candidate should scope and deliver solutions with the ability to design solutions independently based on high-level architecture. They should also maintain the production systems like Kafta, Hadoop, Cassandra, and Elasticsearch.

Qualifications

• The candidate should have a minimum of 4 years of experience in the IT industry.
• They should be technologically wise in Python programming and have proficient skills in Watson NLP.
• He/she should know to develop and embed AI solutions in Intelligent Workflow.
• The candidate should have working knowledge in one NoSQL database like MongoDB, Cassandra, HBase, or Couchbase.
• They should have hands-on experience in big data architectures.

Senior Analyst- Artificial Intelligence Innovation at Accenture–

Location: Bengaluru, Karnataka, India

About the company: Accenture is a global professional services company that provides a range of services and solutions in strategy, consulting, digital, technology, and operations. Combining deep experience and specialized skills across 40 industries and business functions, Accenture works at the intersection of business and technology to help clients improve performance and create sustainable value for stakeholders.

Roles and responsibilities: As a senior analyst- artificial intelligence innovation, the candidate will be aligned with Accenture’s insights and intelligence vertical and help them generate insights by leveraging the latest artificial intelligence and analytics techniques to deliver value to its clients. Generally, the artificial intelligence innovation team at Accenture is responsible for the creation, deployment, and managing of the operations of projects. In this role, the candidate will need to analyze and solve increasingly complex problems. He/she should frequently interact with their peers at Accenture and clients to manage the development well.

Qualifications

• The candidate is mandated to own a B.E/B.Tech/M.Tech degree.
• He/she should have at least 5-8 years of experience in working with artificial intelligence.

Principal, Artificial Intelligence Engineer at AT&T–

Location: Azcapotzalco, Mexico City, Mexico

About the company: AT&T is a US-based telecom company and the second largest provider of mobile services. AT&T operates as a carrier of both fixed and mobile networks in the US but offers telecoms services elsewhere. The company also provides pay-TV services through DirecTV.

Roles and responsibilities: The artificial intelligence engineer at AT&T is responsible for designing and implementing artificial intelligence and machine learning packages, including data pipelines, to process complex, large-scale datasets used for modelling, data mining, and research purposes. He/she is expected to design, develop, troubleshoot, debug, and modify software for AT&T services or the management and monitoring of these service offerings. They should interact with systems engineers t realize the technical design and requirements of the service, including management, systems, and data aspects.

Qualifications

• The candidate is expected to have a Bachelors degree in Computer Science, or Scientific Computing, Data Analytics, Machine Learning, Business Analyst nanodegree, or any equivalent experience.
• He/she should have experience working with Java, Python, SQL, Azure, etc.

Data Engineer at LinkedIn–

Location: Bengaluru, Karnataka, India

About the company: LinkedIn is a social networking site designed to help people make business connections, share their experience and resumes, and find jobs. LinkedIn is free, but a subscription version called LinkedIn Premium offers additional features like online classes and seminars.

Roles and responsibilities: As a data engineer at LinkedIn, the candidate is expected to work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or go to market strategy. He/she should build data expertise and manage complex data systems for a product or a group of products. They should perform all the necessary data transformation tasks to serve products that empower data-driven decision-making. The candidate should establish efficient design and programming patterns for engineers as well as for non-technical partners.

Qualifications

• The candidate is mandated to have Bachelors in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
• He/she should have 3+ years of experience working with a large number of datasets.
• They should have hands-on experience in SQL and relational databases.
• The candidate should have experience with distributed data systems such as Hadoop and related technologies like Spark, Presto, Pig, Hive, etc.

Data Scientist, Engineering at Google–

Location: Bengaluru, Karnataka, India

About the company: Google is an American search engine company founded in 1998. Began as an online search firm, the company now offers more than 50 internet services and products including email, online document creation, software for mobile phones and tablet computers, etc.

Roles and responsibilities: As a data scientist at Google, the candidate will evaluate and improve Google’s products. They will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, bringing analytical rigour and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behaviour of end-users, advertisers, and publishers. He/she should work with large, complex data sets and solve difficult, non-routine analysis problems by applying advanced methods. The candidate should conduct end-to-end analysis, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.

Qualifications

• The candidate should have a Master’s degree in a quantitative discipline or equivalent practical experience.
• He/she should have at least 2 years of experience in data analysis or a related field.
• They should be well-versed in statistical software.

Data Engineer at Tata Consultancy Services-

Location: Melbourne, Victoria, Australia

About the company: Tata Consultancy Services, also known as TCS, is a global leader in IT services, digital, and business solutions. The company partners with clients to simplify, strengthen, and transform their business.

Roles and responsibilities: As a data engineer, the candidate should design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, or Scala. He/she should design and implement data engineering, ingestion, and curation functions on AWS cloud using AWS native or custom programming.

Qualifications

• The candidate is expected to have 4+ years of experience working with AWS cloud, Big data technologies, AWS Cloud Data Services like RedShift, Lambda, Glue, etc.
• He/she should have the experience to build, design, and operationalize enterprise data solutions and applications using one or more AWS data and analytics services.
• They should have experience in ETL Tools, data modelling, data analysis, Python, Shell scripting, ETL Testing, Splunk tools, etc.

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Companies should develop a data science strategy to drive business intelligence.

Business intelligence is not a luxury anymore but a necessity today. The rapid adoption of disruptive technologies has enabled companies to enhance growth and agility. Data fuels businesses in the current scenario and it enables companies to gain intelligent insights. Hence, data science is an important part of regular business processes. Different companies use different methods to optimize data and make better decisions. This is often called a data science strategy. The significance of a data science strategy is immense in driving growth and staying close to the customers.

Why A Strategy?

Creating an effective data science strategy is not as simple as it sounds. Our world is getting smarter every day and businesses need to stay on the competitive edge to achieve success. A data science strategy or data strategy will enable the company to reach the right data, metrics, and data resources with ease and better accessibility. Developing a strategy will need a company to first define its goals, it can be a larger and measurable goal like generating more revenue. The next step is to find the right data resources suitable to the business goal. The executives need to clearly define the questions that they want the data insights to answer so that the company does not end up following unuseful and wrong data. A clearly articulated business strategy can ease the process of developing a data science strategy.

Building A Strategy

As mentioned above, the initial step would be to define measurable goals and find the right data resources. Next is identifying the project infrastructures by understanding which technologies to use, should everything be developed internally or outsourced, etc. The company should also decide the data storage platform and the desired form in which you would like to get insights like visuals, charts, reports, and more. Building a data science strategy also involves deciding the algorithms and technological models that should be used. This includes AI, machine learning, statistical inference, and making clear if these algorithms need to be transparent and explainable.

Another most important step is constituting a data science team. Hiring data scientists can be a bit difficult today as the role is in high demand. The company should analyze if it is going to build an in-house data science team or hire experts from outside. Collecting and storing data will create regulatory obligations that need to be met. Companies should consider data governance as an essential component to avoid data becoming a risk and liability. For this, the data science strategy should include compliance, security measures, and privacy policies in place.

Once all these steps are accomplished, a company can then use data analytics to process the huge amount of data and get actionable insights through disruptive technologies. Data and analytics are the crucial part of business intelligence today and intelligent insights are the only way to understand the audience better and create personalized services.

Data science and machine learning go hand in hand. Machine learning, a subset of AI, is an effective and widely used tool to deliver data analysis and insights. Machine learning models can be fed with data and this will enable the machines to learn from these data and improve from past patterns and risk behaviors. Ensuring data quality often becomes a challenge and integrating machine learning into the data strategy can help overcome it. Machine learning can accurately detect errors with minimal human intervention. Machine learning and data science strategy intersect and this is the current business intelligence scenario. Hence, companies should have a data strategy in place to enhance growth and efficiency.

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Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

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Exploring the growth of AI that are advancing the industries today

Artificial intelligence has transformed the business environment drastically. What began as a rule-based automation system is now capable of simulating human interaction. Artificial intelligence is not only exceptional because of the human capabilities. As compared to human equivalents, an advanced AI algorithm provides better speed and capacity at a much lower price.

Today, we are already related to AI in some way or the other, whether it is Siri, or Alexa, thanks to technological innovations. Yeah, the technology is still in its early stages, but more businesses are engaging in machine learning, suggesting that AI products and applications can expand rapidly in the immediate future.

Here are few examples in which growth of Artificial Intelligence can be seen today:

AI in Content and news organizations

AI systems are increasingly being used by content and news organisations to discover data from various sources and instantly summarize it into content or supporting analysis for those articles. Machine learning algorithms have been shown to be capable of detecting variations in textual data and extracting valuable knowledge that appropriately summarizes the information contained inside. Journalistic organizations can easily keep up with current events and produce content that accurately summarizes evolving circumstances by applying these sophisticated algorithms to vast amounts of data from press releases, tweets, articles, and other unstructured content.

Super Intelligence

BBN Times mentioned that artificial intelligence is not only good at problem solving but it is also demonstrating empathy. AI has developed spontaneous emotions of its own accord. The company Cogito, founded by Joshua Fest and Dr Sandy Pentland, melds together machine learning with behavioral adaptation, supported by the latest breakthroughs in behavioral science.

The result is jarring to those who are used to AI being a benign force ready to work off dry data, but incredibly beneficial in the high-volume customer service arena – and highly promising when we look to the future of artificial intelligence, and how it can continue to improve human life.

AI in Healthcare

The role of AI-powered technologies in next-generation healthcare technology is being recognized by the health sector. AI is thought to have the ability to enhance every phase of healthcare operations and service. For example, the economic benefits that AI can bring to the healthcare sector are a crucial motivator for AI implementation. AI systems are expected to save the US $150 billion in healthcare costs by 2026. The shift from a reactive to a proactive healthcare paradigm, concentrating on health treatment rather than curing diseases, is responsible for a significant portion of these cost savings. As a result, fewer hospitalizations, doctor appointments, and procedures are expected. AI-based innovation will play a key role in assisting people in maintaining their wellbeing by continuous tracking and coaching, as well as ensuring earlier diagnosis, personalized care, and more effective reassessments.

AI in Entertainment

Today the entertainment industry is all set to undergo a different kind of transformation. AI would make the future of the industry more social, personalized, and entertaining. Virtual reality, ally robots can gradually penetrate the home as the use of sensors and hardware expands. Additionally, consumers will be able to converse with entertainment devices.

Media companies will be able to personalize entertainment to previously unthinkable levels thanks to this technology. You’ll possibly be able to build your own entertainment channel using AI in the not-too-distant future.

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