Defining Analytics Titles

Previous entries in Everything Analytics:

The Many Wandering Paths to Analytics
Landing Your First Analytics Job

Confusing Web of Titles

The analytics space is rapidly growing & evolving, and this fast growth has led to a convoluted web of job titles which are overlapping and contradictory. I’m here to help you sort through some of those job titles and even open your eyes to different types of Analytics job you may not have considered. Data Scientists get all the press – but there are many more roles out there which may be a better fit for your interest & skills.

You will rarely find consistency from company to company. In fact, I’ll start with a couple disclaimers:

Disclaimer 1: Keep in mind that my opinion on the separation between these jobs has no bearing on how the HR department of a company defines their positions.

Disclaimer 2: This list is not exhaustive. There are lots of substructures to these roles as well as other data-adjacent or niche jobs which exist.

Keep In Mind When Applying

Make sure you absolutely understand the job description and ask many clarifying questions during interview rounds to fully understand what you’ll be doing. If you aren’t thorough in evaluating the job, you may not end up with the work you thought you’d be doing.

Example Job Titles

Data Scientist
(Related: Statistician)
Data Analyst
(Related: BI Analyst)
Data Engineer
(Related: BI Architect, BI Engineer)
Business Analyst
(Related: Technical Project Manager)
Machine Learning Engineer
(Related: Software Engineer)

Reporting Structure

While there is no one-size-fits-all structure, there are general trends:

Data Scientist/Data Analyst/Business Analyst

These roles may report to any part of the business, depending on how centralized the data organization is. The more centralized, the more likely they are on the same team. Sometimes they may be their own team entirely, rolling to the CEO independent of any other C-Suite leader. Other times they may roll up through the COO or CTO.

Other times, they may be decentralized and be scattered across the company with no specific structure.

Data Engineer / Machine Learning Engineer

Typically these fall under the CTO. Data Engineers may be under IT, or may be their own division. MLEs typically fall into Software Engineering — see below for more discussion.

Detailed Breakdowns

Data Scientist

Overview: This is the most-publicized job title out there and therefore is the broadest; it can mean many things at many places.

Data scientists are forward-looking and focus on predictive analytics. They certainly can do descriptive analytics, but their value comes from modeling/classification/etc.

Due to the emphasis on modeling, data scientists typically have advanced degrees in statistics, applied math, information science, or similar.

Example task: Predict how much stock of each item a company should order from its manufacturers in advance of the holiday season.

Data Analyst

Overview: While Data Scientists are generally forward-looking, data analysts are generally backward-looking and more entrenched in the business. Their job is to help the business understand what has happened up to this point and provide data in a clear & concise way for decision making in the future.

Typically data analysts focus heavily on making visualizations and presentations for the business and bridge the gap between the business and the data.

Data Analysts are also more jack-of-all-trades. It’s common to do a bit of data science, analytics, engineering, and PMing in a single role.

Example task: Create a flexible Tableau dashboard for leadership to track trial conversion to paid users over time

Data Engineer

Overview: Data engineers work on the databases that the other members of the analytics org use to get information to stakeholders. They are responsible for bringing data from the business into some form of data warehouse in an accurate, timely and secure fashion.

This means the typical customers of data engineers are the data analysts/scientists at the company. They also may work directly with different parts of the business as they want their own data ingested automatically into the larger data warehouse.

This role is typically more technical and code-heavy in order to move massive amounts of data around at scale. There is less interaction with the business than other parts of the analytics organization.

Example task: Mirror Salesforce data in a schema in Snowflake, updated every 5 minutes, for analysts & scientists to analyze/visualize

Business Analyst

Overview: Business Analysts are sometimes called a “project/product/program manager”, or PM. No matter the name, they are distinct from Data Analysts/Scientists in an important way. They coordinate and organize data projects across the business.

This role typically doesn’t exist early on in a data team’s existence. Usually individual analysts/scientists take this on until the burden of project managing starts outweighing time spent actually doing analysis. Eventually, the role of Business Analyst comes along.

Business Analysts are not expected to code or be as savvy on the technical side. Rather, their job is to identify problems, gather requirements, allocate resources and coordinate expectations between the data team and the business. This is no small task as many technically minded individuals are great at doing an analysis when there’s a clear question, but struggle to work with non-technical individuals across the organization.

Example task: Sales wants standardized KPI dashboards across their worldwide teams available for next quarter’s SKO

Machine Learning (ML) Engineer

Overview: This is a bonus position added in, largely since I see Machine Learning discussed commonly on Analytics forums and many of you may be wondering how it fits into a data org. The short answer: this role doesn’t fit into the data org per se.

Specifically, this role is commonly found on the Software Engineering (SWE) org and is more of a Software Engineer with an ML focus than anything else. This role is most similar to a data scientist and usually is more involved with implementing models within the production code of the company to solve whatever problem has been identified.

Example task: Predict which users on the website may want to know about Feature X, which will prompt an informational pop-up

In Conclusion

There are all sorts of roles to explore and this list is by no means exhaustive. As I mentioned at the start, the names above may be conflated with each other at any given job you apply to. Regardless, this gives you some guardrails around what sorts of roles are out there — from non-technical to technical and everything in between.