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20 AI Experts You Should Follow

8 minute read | January 30, 2019
Melanie Lawder

Written by:
Melanie Lawder

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As artificial intelligence occupies a larger part of the public consciousness, the media is covering the industry more closely than ever. Keeping up to date on the news, industry trends, and sentiments of influential figures can seem overwhelming.

One way to stay informed is through Twitter. The social media platform is a helpful and entertaining way to stay looped into the musings of some of the top minds and pioneering thinkers in AI, who use their accounts to voice their opinions on the latest news and industry practices.

Our Favorite AI, Machine Learning, and Deep Learning Experts

To get you started, we’ve compiled a rundown of 20 AI, machine learning, and deep learning influencers and experts you should follow on Twitter. This list is a mix of business executives, founders, researchers, and journalists, all of whom bring a unique take on AI to the table.

Elon Musk

Probably the most prolific and famous executive in the field of AI, billionaire Elon Musk is considered a pioneering figure and thought leader in tech, specifically in AI. The CEO of Tesla uses Twitter to shoot off hot takes about industry news and tweet updates about his companies, which also include SpaceX and Neuralink. His tweets typically ignite a ton of reaction from social media users—and sometimes they land him in hot water.

Rana el Kaliouby

Co-founder and CEO of the emotion recognition software company Affectiva, Rana el Kaliouby is consistently named as one of the most influential women in tech. Her firm, which was started out of MIT’s Media Lab, has developed technology that can read a person’s emotional response by analyzing their facial expressions. It’s a technology that has far-reaching implications.  Companies have employed the software to analyze customers’ reactions to their advertising and Affectiva’s technology can also be used to monitor the emotional state of a car driver, gauging when a driver is tired or flustered by a road rage incident.

Related: 25 Influential Women in Tech to Follow Today

Demis Hassabis

You likely heard about Demis Hassabis’ company, DeepMind, when it was purchased by Google in 2014 for more than $500 million. Then, you may have heard about the firm again in 2016 when its AI program AlphaGo beat Go world champion Lee Sedol in a highly publicized televised competition.

DeepMind is often considered to be one of the world’s leading AI companies, and co-founder and CEO Hassabis has a reputation that is just as illustrious. He has been hailed as “the superhero of artificial intelligence” and “London’s megamind” by various media organizations. If you want to stay abreast of DeepMind developments, it would be wise to follow Hassabis on Twitter, where he primarily posts company-related updates.

Andrew Ng

Co-founder of the online educational course company Coursera, Andrew Ng is regarded as one of the most prominent minds in machine learning and deep learning. He’s a co-founder of the Google Brain team and previously led the AI group at Chinese internet company Baidu, which is considered to be the equivalent of Google in China. Presently, he is an adjunct professor at Stanford University. (He’s also married to fellow AI influencer Carol Reiley.)

Carol Reiley

Roboticist and AI expert Carol Reiley co-founded the California startup Drive.ai, which creates self-driving systems for cars and recently launched an on-demand self-driving car service in Texas. Follow her on Twitter and you’ll be looped into links to the latest AI news and industry commentary, along with a few tweets that provide a glimpse into her personal life.

Kai-Fu Lee

CEO of the investment firm Sinovation Ventures and a prominent business figure in China’s technology scene, Kai-Fu Lee is widely regarded as an AI trailblazer. Lee’s resume includes stints at some of the top companies in tech, including Apple, Microsoft, and Google. If you’re an avid watcher of “60 Minutes,” you’ll likely have seen Lee featured in a recent episode, which dubbed the mogul as the “oracle of AI.” Lee sparked controversy when, during the segment, he predicted that AI could displace 40 percent of the world’s jobs in the next 15 years or so. Lee is also the author of the recently published book “AI Superpowers: China, Silicon Valley, and the New World Order.”

Animashree Anandkumar

https://twitter.com/AnimaAnandkumar/status/1089221678654050304

Animashree Anandkumar is the director of machine learning research at Nvidia and her tweets relay informative AI articles along with job opportunities at the California maker of graphics processing units. Anandkumar, whose resume boasts an impressive array of awards and accomplishments, also holds the distinction of being the youngest named professor at Caltech, where she teaches in the Computing + Mathematical Sciences Department.

Joy Buolamwini

The founder of the Algorithmic Justice League, Joy Buolamwini is a digital activist and is considered to be the foremost expert in algorithmic bias, or what she calls the “coded gaze.” Her recent work exposing racial biases in Amazon’s facial recognition technology was documented in articles by The New York Times and other news outlets. Buolamwini’s MIT thesis, titled “Gender Shades,” also made waves with the revelation that facial analysis technology created by firms like IBM and Microsoft was more likely to misclassify the gender of women and darker-skinned individuals. She’s also been featured among BBC’s 100 Women in 2018 and Forbes’ list of America’s Top 50 Women in Tech. Follow Buolamwini on Twitter to keep up with her research and for analysis on algorithmic bias in AI.

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Andrej Karpathy

As the director of AI at Tesla, Andrej Karpathy is a well-known figure in the world of AI, machine learning, and deep learning. His timeline is filled with personal insights and industry observations, along with more technical content for those interested in the nitty-gritty. (His blog is also well respected in the community.)

Related: 40 Must-Read Machine Learning, AI, and Deep Learning Blogs

Geoffrey Hinton

Geoffrey Hinton just joined Twitter in January 2019 and has only tweeted a few times. But given his reputation as the “godfather of deep learning,” Hinton is one of those obligatory AI figures you’ll want to keep tabs on. Hinton, who is revered for his work on artificial neural networks, is presently a professor at the University of Toronto and works at Google. We’ll be eagerly watching his Twitter for his insights.

Fei-Fei Li

Stanford professor and AI guru Fei-Fei Li was the head of Google Cloud, but left the tech company in late 2018 to head up Stanford’s Human-Centered AI Institute, which is predicated on the idea that the development and shaping of AI technology must be humanistic as well as technological. Her timeline is a blend of industry news, personal development, and other related content.

RelatedAI for Social Good: 7 Inspiring Examples

Moustapha Cisse

The head of the Google AI Center in Ghana, Moustapha Cisse is an AI thought leader and an advocate for greater diversity in the industry. A believer that AI can help improve lives and reduce global inequalities, Cisse is a co-founder of the group Black in AI, which is a community of AI students, researchers, and experts who aim to increase the representation of black people in the industry. He also recently co-founded the African Masters in Machine Learning, a one-year degree at the African Institute for Mathematical Sciences, and was named one of Quartz’s Africa Innovators 2018.

Karen Hao

Karen Hao is the AI reporter for the MIT Technology Review and her Twitter timeline includes her latest works as well as links to other relevant AI news articles and industry analysis. An engineer by training, she’s also the author of the Technology Review’s AI newsletter, The Algorithm.

Related: 20 Machine Learning, Data Science, and AI Newsletters That Will Keep You Informed

Timnit Gebru

Computer scientist Timnit Gebru is an advocate for inclusion in AI and her work has addressed and uncovered racial and gender bias in AI systems. She is also a co-founder of Black in AI, a research scientist on the Ethical AI team at Google, and a co-author of Gender Shades with fellow influencer Joy Buolamwini. Follow her on Twitter for her perceptive commentary on algorithmic biases and other AI-related news.

Kate Crawford

If you’re at all concerned about the implications that AI will have on society, then you may want to follow Kate Crawford on Twitter for her expertise in the area. Crawford is co-founder and co-director at the AI Now Institute—a research institute housed at New York University and dedicated to studying the social impact of AI. She uses Twitter to offer up insightful and sharp-witted commentary on the ethical implications stemming from the application of AI, as well as compelling takes on broader tech news.

Jeff Dean

The head of Google’s AI unit, Jeff Dean is one of the earliest employees of the tech company and one of the founders of the Google Brain team. His Twitter consists of links to interesting research, the latest Google AI developments, and other industry news.

Mariya Yao

https://twitter.com/thinkmariya/status/1012096471523569665

As TOPBOTS’ editor-in-chief, Mariya Yao has her finger on the pulse of the latest applied AI and machine learning content. You can expect the same with her Twitter timeline, which is chock full of news articles, helpful resources, and other commentary related to AI and tech. In addition to her position at TOPBOTS, Yao is also the chief technology and product officer at Metamaven and co-author of the book “Applied Artificial Intelligence: A Handbook For Business Leaders.”

Rachel Thomas

Rachel Thomas is a co-founder of fast.ai, an online community platform that offers free courses for coders, a software library, and a forum where AI enthusiasts can collaborate. In sum, fast.ai aims to get more people to understand and become involved in deep learning. And, in many ways, Thomas’ Twitter is an extension of that brand. Follow her on Twitter and you’ll find links to helpful AI educational resources, fast.ai updates, news articles, along with other smart analyses related to AI. Thomas is also currently a professor at the University of San Francisco.

Jack Clark

As policy director at the nonprofit AI research institute Open AI, Jack Clark uses his Twitter to share the latest research and developments in AI, as well as thought-provoking analysis on the technology’s societal, policy, and geopolitical implications. If you follow him, you can expect shrewd and succinct takes on why certain AI news matters. Prior to Open AI, Clark was a reporter at Bloomberg, where he covered AI; he is also the curator behind the meaty newsletter Import AI.

Cade Metz

A technology correspondent at The New York Times, Cade Metz covers the AI beat relentlessly and thoroughly. He was previously a journalist at WIRED and his Twitter is filled with his latest scoops.

Machine learning, on the other hand, is a subset of data science. In addition, future machine learning engineers, and aspiring data scientists should follow these experts to stay updated.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Melanie Lawder

Melanie is a Milwaukee-based freelance writer. She has reported for publications like the Milwaukee Business Journal and the Wausau Daily Herald. Follow her on Twitter @mel_lawder.