4 Types of Data Scientists
Data Scientist is apparently the sexiest job title of the 21st century, according to the Harvard Business Review anyway, but what even is a Data Scientist? Many people think Data Scientists work on AI and build models that can kick our butts in go, but the vast minority of Data Scientists are actually working on those types of problems. Maybe you’re looking to become a Data Scientist yourself or you’re looking to hire one, If so keep reading because I will cover four different types of data scientists as well as questions to ask yourself to see if each type is right for you.
- Data Translator
photo by neONBRAND
This type of Data Scientist is often referred to as a Data Analyst, and they often work for very large companies. I call them ‘translators’ because these people are able to communicate with executives, managers, other departments, and any external stakeholders, all of which are unlikely to have a technical background, and identify what they want to know about their data speaking in business terms. They can then go into the database, created by the Data Engineering team, to query the data needed to answer the questions and then present their findings in a way that their audience understands. These Data Scientists rarely, if ever, use Machine Learning models and are more focused on hypothesis testing and statistics.
Sample business case: You work for a large insurance company, they have launched a mobile app and are seeing a decrease in revenue since it’s release. They need you to analyze the conversion and drop-off throughout the sales funnel and compare it to that of their website, this way they can find the weak point in their mobile app.
Skills (to develop in yourself or look for in others): Querying Language (i.e. SQL), BI Tools/ Data Visualization(i.e. Tableau), Programming Language (i.e. Python), Statistics, A/B testing, presentation & communication, Business Sense, will also need to know Data Structures and Algorithms very well if you plan on working at a FAANG-M (Facebook, Apple, Amazon, Netflix, Google, Microsoft)
Is it for you?
- You have a nontechnical background. Maybe Economics Finance, or Marketing?
- You’re not crazy about Machine Learning
- You dream of working at a FAANG
- You love making colorful graphs, charts, and other visualizations
2. Classic Data Scientist
This is what I think of when I think of Data Scientist! These roles include the roles of the Data Translator, so in a sense they too are Data Translators, however they work for mid sized companies most often in tech startups and software companies from 200 up to maybe a few thousand employees. The distinction in their roles is that they often use Machine Learning to make predictions, classifications, recommendations or find anomalies. Some may even work with generative models to make chatbots! They also tend to work in a more siloed environment or in a small team. Many companies lack the internal data to complete projects they need and will often have to collect data from external Data Vendors, although they will likely have Data Engineers doing this. A final note on the Classic Data Scientist is that they have years of experience, almost all have a Masters and many have a PHD (in STEM areas) although some bootcamps out there can serve as an effective alternative to Grad School. This is a contrast from the Data Translator who can typically get hired with a somewhat relevant bachelor’s degree coupled with self education in Data Analytics.
In Summary, These Data Scientists will query data and preform analysis just like the Data Translators. The difference is that they build models to make predictions, classifications, recommendations, or flag anomalies.
Sample Business Case : Tala is a fintech that helps people in developing countries with no financial backgrounds get approved for loans often for business or education. The Data Engineers seek out alternative data such as internet use and social media data of their customers, the Data Scientist queries the data, performs analysis, builds models, might even do some NLP, and ideally finds a way to predict if a customer could get a loan without using financial data.
Skills : Querying Language (i.e. SQL), BI Tools/ Data Visualization(i.e. Tableau), Programming Language (i.e. Python), Statistics, presentation & communication, Business Sense, Machine Learning and a framework (i.e. SciKit Learn), Deep Learning and a framework (i.e. Tensorflow), Machine Learning Intuition, Cloud Deployment (i.e. AWS)
Is it for you?
- You have accidentally come across Data Science Techniques in your work or study and really enjoy it
- You have a Scientific Background
- You’re experienced in a highly technical role, love Machine Learning, and want to be more on the business side
3. Full Stack Data Scientist
This is quite similar to the Classic Data Scientist, the difference is that they take on the entire data pipeline which includes more Data and Software Engineering tasks. They often work for very small AI startups, in fact many are Cofounders. They Carry the weight of the company on their shoulders because the models they build are often the core of the product / service rather than say increasing a webpage’s click-through-rate by 5% or what have you. These companies tend to have a very scrappy culture; move fast, fail fast, learn fast. Because of this, alongside having a very low budget, the companies rarely if ever require formal education and thus a Full Stack Data Scientist doesn’t need any formal education. Being a Full Stack Data Scientist comes at the expense of low pay and long hours but the benefit of having near 100% control of how you do your job.
It’s interesting because companies and Hiring Managers have been known to look for the perfect Data Scientist that doesn’t exist. The Full Stack Data Scientist is the one that actually has the full breadth of skills that some of these erroneous job listings have, however their lack of experience in established companies, education, and in depth knowledge would make them highly unattractive candidates.
Sample Business Case : Rares is an app startup that predicts the value of luxury sneakers and allows people to trade luxury sneakers, just like they would stock shares. They need a Data Scientist to find relevant data, build models that predict the price of the sneakers, and deploy the models. It may sound easy but we all know how startups go :)
Skills : They should emphasize on Machine Learning and Data Analysis but ultimately they should be prepared to learn just enough about anything to make it happen!
Is it for you?
- You feel bored at a comfortable 9–5
- You love startups. Move fast and break things!
- You’re ok working 80 hours a week and living off ramen. Hopefully you can find a healthier but equally cheap alternative :)
4. Research Scientist
Research Scientists, also known as AI researchers or Machine Learning researchers, most often work at R&D departments at FAANG-M companies as well as research focused companies like OpenAI or Deepmind. Some work on recommendation systems and text to speech applications that we know all love, Think Siri, Alexa, that up next on Netflix that you can’t turn down. While others work to push what’s possible and develop new models and concepts like Reinforcement Learning , GPT2, and GAN’s and publish Scientific Papers. Some work with Engineers to build API’s like Hugging Face that allow the general public (Data Scientists, ML Engineers, and AI enthusiasts)to access these models. The vast majority of Research Scientists have PHD’s, typically Computer Science, but some Master’s have been hired with enough experience in Scientific Research.
Sample Business Case : This blog says it better than I can :)
Skills : They should know Statistics, Algebra, Calculus, Machine learning, and Deep learning very well, Programming (i.e. Python or C++)
Is it for you?
- You are very academic and want to go the PHD route
- You‘re passionate about scientific research
- You want to do stuff that’s never been done before
Wrapping it up
So now you know about four major types of Data Scientists, I hope this gives you more clarity in your data journey. In general, I feel that Data Analyst is a more practical route especially for young professionals looking for a stable career path. If your more experienced in technical roles like Data Analysis, Software Engineering, or have a scientific background and love Machine Learning then Data Scientist may be more for you. If you are absolutely crazy about Data Science, don’t want to climb the ladder, and are willing to give up work life balance then find an AI startup that solves a problem you’re passionate about, heck you could even start one! If you’re interested in the AI research route and don’t mind working on theoretical concepts and are very academic than maybe Research Scientist is for you.
Please let me know what you think and feel free to connect on LinkedIn!
