Every company defines roles differently, but this is a start to help you choose the right path!

Whether I talk to people interested in a data job who are still in college, just finished their Master’s, or are looking to make a career pivot, my first point of advice is always figuring out what job family will be the best fit given skillsets, interests, and experience. From there, it is much easier to identify skill matches and potential career paths. Not everyone has a straight career path or fits in a particular bucket, so it is more helpful to keep job family general frameworks in mind when figuring out what is next.

This guide is also helpful for non-tech roles to better navigate business partnerships and stakeholders.

Business Analyst/Data Analyst

A Business/Data Analyst is more of a generalist with the technical skillset of Excel, Tableau or similar reporting software, and SQL. Some Business/Data Analysts have more strengths in business process improvement, while others are more advanced in their technical skillset. Responsibilites may include running automated Excel reports with Macros/VBA, creating and monitoring metrics/reports in a reporting software like Tableau using SQL queries, creating forecasts, prototyping models, performing ad-hoc analyses, and communicating insights in business reviews. Business/Data Analysts will use both technical and non-technical skills to deep dive into big organizational questions. It is traditionally a first role before transitioning to some of the others listed, for those who do choose to transition.

Business Intelligence Analyst/ Business Intelligence Engineer/Analytics Engineer

I will refer to this job as BIE, but know that I am referring to the three different names above seen across the industry. A BIE wears many hats, and is traditionally the next step after being a Business/Data Analyst. Responsibilities range from designing and developing productionalized reporting/dashboard solutions, automated tooling, engineering solutions, and ETL jobs. BIE’s are also fluent in optimizing data and building self-serve data products for customers.

Data Engineer

I personally think Data Engineers are the backbone holding up data teams, and think it is one of the most critical jobs for an organization’s success. Data Engineers own and maintain the core data infrastructure for teams. Especially as a Business/Data Analyst or Data Scientist, Data Engineers will be your favorite people. They architect and design scalable data warehousing, ETL pipelines, data pipelines, team tech stacks, and data governance. They help to source raw data whether it’s from APIs, Salesforce, another team’s data warehouse, or data store. From there it is tranformed and automated into a useable state.

Data Scientist

This is my role, so I will expand on this one more than the others as I have additional context. Data Scientists probably have the most range in how it is defined across companies in the tech industry. Some companies have rebranded their roles where a Data Scientist’s responsibilities are more in line with a Business/Data Analyst job title, and a traditional Data Scientist scope then shifted to a Research Scientist or Machine Learning Engineer job title. See an example of Lyft communicating this change in its blog here. Many other companies have also taken this approach.

However, if you are looking to find a role within the traditional framework, it is important to know the distinction, especially if you plan on utilizing/diving deeper into your technical skillsets. This can easily be identified in an interview process by simply asking your recruiter for the company definition, and based on the technical assessment you receive. I myself have experienced both sides of this role, and it is important not to pigeonhole yourself in an analytics role if your passion lies in machine learning and sophisticated solutions. For those who enjoy staying in the analytics space, there are also many options to explore in your career path, especially in the analytics leadership route.

For a traditional data scientist role, responsibilities include creating and productionalizing machine learning models using Python or R in notebooks, performing experimentation through A/B testing and hypothesis testing, developing statistical analyses, and communicating insights through scientific artifacts/papers. They must have an understanding of business intelligence/data engineering as sometimes projects require working with terabytes or even petabytes of data. For a more analytics aligned role, refer to Business/Data Analyst above.

Machine Learning Engineer

A Machine Learning Engineer has a niche focus on productionalizing models/algorithms, maintaining machine learning infrastructure, and monitoring machine learning systems. If this role exists, the data scientist will pass off the model to the Machine Learning Engineer for model optimization, containerization, deployment of models, and improving model performance. If the role does not exist, normally this is the job of the data scientist in the final stages of a project.

Research Scientist

A Research Scientist is normally an expert in a specific research function. Many may come from academia, but there are plenty who come from other sectors and backgrounds. They innovate with new methodologies, form hypotheses, and design research studies. They will prototype and simulate models leveraging their research. The definition of this role will vary a lot across companies.

Applied Scientist

An Applied Scientist builds upon the data scientist/research scientist role, but is also fluent in software engineering skills. They will create a machine learning model or statistical analysis, and build systems and deploy them using their combination of programming skills in C++, Java, and Python. They focus on the implementation stage of the process, and normally work on very large scale machine learning/software products. The definition of this role will also vary across companies.

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