These are the top freelance jobs for Artificial intelligence professionals

Artificial intelligence is gaining a lot of attention recently. Industries are implementing more AI solutions and tools because it enhances efficiency, reduces manual labor, and helps businesses earn better revenues simultaneously. In areas like medical diagnosis, speech translation, accounting, AI has outperformed humans in every possible way.

AI has not only generated profits for businesses but has also created numerous job opportunities. Recent studies reveal that individuals from both tech and non-tech backgrounds are aspiring to pursue a career in artificial intelligence, which will also fulfill the growing demands of skilled AI professionals in the tech industry.

This article provides a list of the top freelancing jobs for individuals aspiring to pursue an AI career.

• R Algorithm Developer: This job profile requires the candidate to perform machine learning operations like classification and regression. R programming provides various packages and features for developing AI neural networks. According to Toptal, the estimated length for this job role provided by a marketing and advertising company is 1-2 weeks, on a part-time basis.

• R Mentor: This opportunity is provided by a technology company with global operations and high-end partners. The company is looking for a mentor to work on R programming and assist others to help approach the problems concerning R. The estimated length for this job role is 2-4 weeks with remote working facilities.

• Model Interpretability Machine Learning: According to Upwork, for this job role, the candidate has to build a machine learning model on a provided database by the recruiting company. The company will allow the selected candidate to choose from a list of platforms, according to one’s proficiency, based on which the application will be developed. The estimated period for this job is less than a month and allows remote working.

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• AI Project Manager: This job entails working on implementing AI and machine learning onto e-learning platforms. The company is looking for a project manager who will assist and supervise a team of AI The estimated duration for this role is 3 to 6 months for 30 hours per week.

• Software Developer to develop Research and Analysis Tool for Amazon: The company is looking for a full-stack software developer/web developer to make a research and analysis tool for Amazon. The tool would be cloud-based with front and backend development and CRM management. The duration of this role will be discussed after the candidate is hired.

• Data Scientist: This job requires an AI professional with over 4 years of experience in the field to develop a recommendation engine. The candidate must be familiar with different programming languages like R, Python, and Spark, and should also be adept in natural language processing. Excellent communication and collaboration skills will be a bonus to get hired. The duration of this job is 3 to 6 months with less than 30 hours of work per week.

• Topic Modeling (NLP) in Python: For this job, the candidate has to carry out topic modeling of reviews using LDA or any other procedures best suited for the job. The candidate has to use the trial-and-error method until an optimal number of the most distinct topics is reached. The candidate also has to use AI algorithms to classify new reviews into topics. The company is looking for someone with intermediate experience in the field and is providing remote working facilities.

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AI jobs are Growing in Demand. Know which one suits you the best-

Artificial intelligence is taking over the tech space. AI-powered systems and applications are dominating every aspect of our lives. Tech leaders are constantly innovating and implementing AI technologies in business operations to drive growth and enhance customer satisfaction. Owing to the rising demand for artificial intelligence technologies, the tech industry is demanding skilled professionals; who can manage the new techs and help the companies process their operations efficiently. AI jobs have taken a turn and, skilled candidates are flocking towards the tech industry.

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Here are the top 7 AI jobs for individuals interested in pursuing AI careers.

1. Intelligence Designer: Intelligence Designers are AI professionals who decide, manage, and estimate the strategic choices of how, when, and where to develop AI technologies based on complex and large IT systems. An intelligence designer needs to see the bigger picture and localize all the intelligence to understand and curate a strategy for the correct procedure to develop AI systems.

2. Data Evangelist: The role of data evangelism will become crucial for the adoption and growth of AI solutions. Companies soon will invest heavily in data evangelist roles to focus specifically on working across the organizations to educate users about the available AI solutions, the importance of data-driven decision-making, and the different ways to smoothly change the traditional business workflows to take advantage of the emerging technologies.

3. Machine Learning Engineer: The role of a machine learning engineer is currently highly sought after because of the rise in demand for machine learning technologies and programs. This role is compatible with candidates who have a degree in data science. The ML engineer will have to be adept in the various programming languages, as well as in AI programming.

4. Research Scientist: In the AI industry, the role of a research scientist is crucial. A research scientist needs to have a deeper understanding of the multiple disciplines of AI, like computational statistics, machine learning, deep learning, applied mathematics, and other related fields. To get hired for this job, one needs to have extensive knowledge about NLP, graphical models, and reinforcement learning.

5. AI Interaction Designer: An AI interaction designer is responsible for making sure that anything that the AI agents say is responsible, sensible, and coherent to the problem at hand. In the IT industry, there will be a growing need for professionals who can make AI systems usable for the mass audience.

6. Data Curator: A data curator uses self-service data platforms to curate data for different analytical tasks, allocates computational resources for accelerating data analysis, adds semantic data to the data catalog to speed up the analysis of high-value datasets, and blend data sets.

7. Business Intelligence Developer: The primary goal of a business intelligence developer is to analyze complex datasets and identify business and market trends to boost business revenues. The BI developer will use cloud-based data platforms to design, model, and maintain complex datasets. The candidate needs to have experience in data mining, data warehouse design, SQL integration services, SQL queries, and other related fields.

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Organizations are investing heavily in various data science technologies to augment their Big Data analytics and predictive intelligence capabilities. But, the growing investments are putting immense pressure on the existing data science teams and resources. According to a recent report, 96% of teams have reported they are already at or over their work capacity.

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Ascend.io, the data engineering company, announced results from its second annual research study, The 2021 DataAware Pulse Survey, about the work capacity and priorities of data teams, including data analysts, data scientists, data engineers, and enterprise architects. Conducted in Q2 2021, findings from more than 400 U.S.-based data professionals reveal key insights on their teams’ current workload, productivity bottlenecks, and their organizations’ accelerated need for data products. Demonstrating the current pressures many data teams face, the research found that 96% of teams are at or over capacity – down only 1% from the 2020 study.

Business Demands for Data Projects Accelerate Faster Than Team Capacity

The vast majority (93%) of respondents anticipate the number of data pipelines in their organization to increase between now and the end of the year – with 56% projecting the number of data pipelines to increase by more than 50%. This surge appears to be affecting some roles more than others, with data scientists 2.4x more likely than other roles to anticipate significant growth in pipeline demands.

Encouragingly, the majority (79%) of respondents indicated that their infrastructure and systems are able to scale to meet their increased data volume processing needs, which further highlights that the new problem with scale is focused on team capacity and less on technology capacity.

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Amid the significant increase in the number of data pipelines across their organization, 74% of data professionals indicated their organization’s need for data products is growing faster than their team size. This was especially true for data engineer respondents, of which 81% cited the need for data products was increasing at a faster rate than their team size.

“Data pipelines are fueling nearly every data-driven initiative across the business,” said Sean Knapp, CEO and founder of Ascend.io. “However, as innovations at the infrastructure layer continue to enable processing of greater volumes and velocities of data, businesses face a new scaling challenge: how to enable their teams to achieve more, and faster. Our research shows that team sizes are not scaling at a fast enough rate to keep up with the needs of the business. Combined with our data that highlights almost every data professional today is already at capacity, this leaves little room for strategic work and innovation.”

Lack of Data Engineering Resources Leads to Downstream Delays

When asked which tasks in their data ecosystem are the most backlogged, respondents were significantly more likely to identify their own function of the business compared to their peers – with enterprise architects 2.7 times more likely to indicate data architecture as the most backlogged, data analysts 4.5 times more likely to indicate data analysis, and data scientists 5.1 times more likely to indicate data science. Data engineers were an astounding 7.0 times more likely to indicate data engineering as the most resource-demanding function of the business.

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Further evidence points to a growing backlog of data engineering tasks across teams. In fact, the most common bottlenecks across teams include the maintenance of existing and legacy data systems (39%), data system setup and prototyping (30%), and need to ask for access to data or systems (26%). Among the respondents, data scientists reported the greatest need to ask for access to data at 39%.

Knapp continued, “Across the board, our research shows that data engineering has become a significant roadblock for many teams. Simply put, data engineers are overburdened with building and maintaining mission-critical yet fragile pipelines. This can lead to significant delays for downstream users, as well as drive many teams to address data engineering bottlenecks by attempting to self-serve the data they need access to, often with mixed results, depending on the maturity of their in-house platforms. As a result, each team becomes overwhelmed with their own workload – a dynamic that can quickly lead to organizational friction, if not properly addressed, as well as inhibit the ability to meet the data needs of the business.”

Teams Turn to Automation, Yet Are Wary of No-code Tools for Data Science

Data teams are seeking technology solutions to overcome their bandwidth limitations and do more, faster. When asked how they plan to increase bandwidth across their team, more than half of respondents plan to buy new products or tools (53%) or implement automation technology (53%). Additional means to increase bandwidth include hiring more staff (47%) and re-platforming and retiring legacy technologies (30%).

As data teams evaluate new solutions, concerns remain around the usability of low- and no-code technology. According to the 2020 study, 80% of respondents reported they were already using or considering low-and no-code solutions to increase their team’s bandwidth. Interestingly, the 2021 research indicates that only 4% of data professionals prefer a no-code user interface. However, that number jumps to 73% if the solutions offered the flexibility of using both low- and no-code user interfaces in conjunction with higher code options, signaling a tremendous surge in interest for flexible coding (flex-code) approaches.

“In recent years, there has been a marked increase in the number of low- and no-code technologies available in the market,” said Knapp. “Despite the many benefits of these tools, businesses today are quickly discovering they also carry serious limitations. Many low- and no-code solutions don’t allow teams to customize code when needed for more complex business logic. The end result is that while making 95% of the job easier, the last 5% becomes impossible. With this in mind, it’s no wonder that 73% of data teams indicated they would be more likely to use a low- or no-code tool if it offered the ability to use their preferred programming language.”

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The list represents top companies hiring data science professionals in Switzerland in March 2020

The world is powered by exponential data. With structured and unstructured data growing dramatically in companies’ data centers, organizations are looking for professionals who can work on sorting the data and make insightful decisions from it. This has led to a spike in the demand for data science professionals. The data science field is drastically evolving and the need to fill data science jobs is also growing. It is no different in Switzerland. Data science in Switzerland is also ballooning and the leading tech players are looking to hire data science professionals. Data scientists manage, mine, and clean unstructured data and prepare it for practical use. It is later used to deliver results that impact business outcomes. Some of the common data science jobs in Switzerland include data scientist, data analyst, data engineer, enterprise data architect, research scientist, etc. Analytics Insight has listed top companies hiring data science professionals in Switzerland in March 2020.

Data Scientist at Tata Consultancy Services

Tata Consultancy Services Ltd is an IT services, business solutions, and outsourcing organization that delivers real results to global businesses, ensuring a firm level of certainty. Founded in 1968 and initially based in India, the company now has a global presence, deep domain expertise in multiple industry verticals, and a rich portfolio of services such as consulting and service integration, digital transformation services, and cognitive business operations.

Job role and responsibilities: As a data scientist, the candidate should support the analysis of the data in Risk and Compliance area. He/she should prepare additional big data and data analysis technologies within the department, like data modelling, wrangling, pipelines implementation, etc. The candidate should build machine learning models on Palantir Foundry stack. He/she should review and methodologies to match clients via name matching.

Research Scientist at Microsoft

Location: Zurich, Switzerland

Microsoft Corporation is an American multinational technology company headquartered in Redmond, Washington. Microsoft is the leading developer of personal computer software systems and applications. The company also publishes books and multimedia titles, produces its own line of hybrid tablet computers, offers email services, and sells electronic game systems and computer peripherals. Founded in 1974, Microsoft is now well established across the globe.

Job role and responsibilities: The research scientist will work with an interdisciplinary team from conducting state of the art research to developing early prototypes to productization of technologies for social presence and action understanding in mixed reality. He/she should engage with the research community through publications, workshops or the supervision of interns & PhD students. The company demands the candidate to be self-motivated, strong as a team-player, should be a good communicator, and must accept challenges.

Data scientist at eBay

Location: Zurich, Switzerland

eBay Inc. is a global commerce leader that connects millions of buyers and sellers around the world. Founded in 1995, the company enables the economic opportunity for individuals, entrepreneurs, businesses, and organizations of all size. eBay portfolio of brands includes eBay Marketplace and eBay Classifieds Group, operating in 190 markets across the globe.

Job role and responsibilities: As the role is data-intensive, the data scientist is expected to be comfortable working with large sets of data and deliver business insights from the data using analytical techniques. He/she should be able to offer a strategic perspective, sound business judgement, and willing to work collaboratively with multiple teams in different geographies. The potential candidate should have a hands-on approach to data analysis, machine learning, strong intellectual curiosity, and a passion for achieving practical business impact.

Research scientist at DFINITY

Location: Zurich, Switzerland

The DFINITY Foundation is a not-for-profit organization based in Zurich, Switzerland, that oversees research centers in Palo Alto, San Francisco, Tokyo, and Zurich. The foundation’s mission is to build, promote, and maintain the internet computer and improve the world by doing so. The internet computer extends the functionality of internet from connecting billions of people to also providing million of developers and entrepreneurs with a public compute platform, creating a revolutionary new way to build websites, enterprise systems and internet services within an open environment.

Job role and responsibilities: As a research scientist, the potential candidate should collaborate with engineers to develop secure, reliable and scalable solutions, from problem identification and solution conception of production. He/she should provide deep technical insight in a field of interest and communicate it clearly. The candidate should contribute to designs and specifications of the next iterations of the Internet Computer.

Data engineer at SystemsAccountants

Location: Zurich, Switzerland

SystemsAccountants is a highly specialist recruitment and resourcing solutions company, focused exclusively on financial, accounting technology and transformation. The company specializes solely in sourcing, vetting and placing professionals who have a hybrid combination of financial/accounting experience and qualifications, blended with technology, project and change management expertise.

Job role and responsibilities: As a data engineer, the potential candidate will join fellow data enthusiasts, specializing in data engineering and integration. The team is responsible for integrating a wide range of Information Systems and helping make the best data available to the Data Insight team. The role involves significant projects while also managing support of existing ETL and Data pipelines.

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Artificial Intelligence (AI) is revolutionizing the way we live and work , so today I invite you to learn more about this topic by approaching it from three different and increasingly complex segments.

Broadly speaking, we can refer to AI as the simulation of human intelligence by machines . In other words, a discipline that tries to create systems capable of learning and reasoning like people .

Importantly, Artificial Intelligence is the most debated technology of the 21st century . Today it is widely used to solve complex problems and facilitate human tasks.

Do you still have doubts about what artificial intelligence is? So join me in developing the following segments.

Segment 1: Explanation for elementary students

Imagine a machine that organizes your closet the way you like it , or that serves refreshments to your friends during a party . That is, a robot that performs the activities that you do daily. Well, this is precisely what Artificial Intelligence makes possible. Through algorithms and mathematical functions, AI provides machines with intelligence similar to that of a human to perform everyday tasks such as playing soccer with you or even giving you some dance classes

Through Artificial Intelligence, machines can learn, reason and solve problems . Three capabilities that make the robot artificially intelligent.

Segment 2: Explanation for Professionals

Artificial intelligence works by combining large amounts of data with fast, iterative processing, as well as intelligent algorithms, which allow software to automatically learn and identify patterns . The goal of AI is to create systems that can function intelligently and independently of human beings AND perform tasks in the professional field for example in the construction of buildings or in factories automated by intelligent robots.

Artificial intelligence has subfields, among which the following stand out:

Natural language processing : it is the ability of computers to analyze, understand and generate human language, including speech. Some products in this area are Amazon Alexa, Google voice

• A neural network : it is a type of machine-based learning that is made up of interconnected units (such as neurons), which process and transmit information.
• Cognitive computing : here a human-type interaction with machines is sought. Using artificial intelligence and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech. Some applications in this area are autonomous vehicles in which companies in Silicon Valley California work such as Cruise , Zoox and Nuro.

Segment 3: Explanation for Scientists

To develop this segment I am going to focus on the fact that Artificial Intelligence allows creating machines that can solve problems as people do . The interesting thing here is to compare them with humans and understand how they interact with them. To do this, the following points must be taken into account:

It is worth mentioning that there are currently4 types of Artificial Intelligence , according to Professor Arend Hintze :

Purely reactive: they do not have the ability to form memories , therefore they cannot use their experience in decision making. These are machines that make predictions based on parameters. An example of this type isDeep Blue , the IBM-made supercomputer that defeated chess grandmaster and world champion Garri Kasparov in 1997.

Limited memory: machines of this type do store memories and use that “past” to make decisions , that is, they use the knowledge of their previous experiences to elaborate a response. However, this information is kept for a certain time, so their vision of the past is very “limited”. To put it in some way, they store only a limited number of recent experiences that they use to perform their calculations and act on the environment, they do not use that experience to learn it forever, as people would. An example of this type is autonomous cars.

Theory of mind : one of the objectives of Artificial Intelligence is to ensure that machines can emulate the learning process that people have. The machines of this classification are then more advanced, they are capable of processing and expressing emotions . Theoretical knowledge of these emotions allows them to know how people or elements in their environment think and feel, and to predict what we expect them to do, to later adjust their behavior according to those predictions.

Self-awareness : this would be the highest step in the hierarchy proposed by Professor Hintze . In it would be the machines that are aware of themselves, recognize their internal states and become aware of the place and position they occupy within an environment. In this case they could not only predict behaviors but also emotions because they would understand what these entail in all its complexity.

Amazing isn’t it?

As we can see in the three explanatory segments above, Artificial Intelligence is designed to work with humans and facilitate their daily tasks . We currently find it applied in: banks, online customer service, cyber security, virtual assistants, smartphones, cars, social networks, video games and in many other aspects of daily life.

This panorama allows us to see that in the near future we will no longer think about how to use emerging technologies, but we will have to learn to relate to them and live with machines in a natural way .

If you want to learn more about Artificial Intelligence, I invite you to be part of Ai Lab School , the first practical education program in AI programming in Mexico . The course lasts 9 months , where you learn, based on projects, to develop solutions using advanced algorithms with AI. Once the course is finished, you access the next stage: job placement . Here the Ai Lab School team guides and helps you to apply for job offers in Silicon Valley and in technology hubs in the United States, in collaboration with the Mexican talent connection program in innovation and technology companies and startups Talentum Space .

• Speech recognition , an important point today
• Object detection
• Solve problems and learn from the data provided
• Planning approach for future tests to be performed

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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.

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