Data Analyst vs Data Scientist: Industry Perspectives

Both “Data Analyst” (DA) and “Data Scientist” (DS) are titles that vary greatly between industries and even amongst individual organizations within industries. As the roles behind titles change over time, it is natural for some teams to ask themselves the following questions: should we have distinct roles or just stick to one? How would we differentiate the roles in a way that fulfills our organization’s needs and is generally consistent with similar organizations? Do we want to consider a DS to be equivalent to a Sr. DA, the only difference being the title? Answering these questions not only establishes clear responsibilities and expectations, but enables hiring managers and recruiters to communicate clearly with potential applicants in the future (in job postings, for example).

Search the Internet for “data scientist vs data analyst” and you will find plenty of people who don’t know what the difference is (nor if there even is one anymore), and you will find plenty of people who think they know the definitions and differences. You will find an abundance of opinions but very little consistency! When I asked my followers on social media what they personally think the differences are, not everyone shared the same opinion but some interesting camps of thought emerged.

This is my effort to summarize the many replies I received, so here are certain important points, recurring themes, and somewhat overlapping camps of thought:

  • Single/primary distinction: DS is a DA who can code
    • In summary: the kinds of questions that a DA can answer and the kinds of tasks a DA can work on are a subset of DS’s because GUI tools limit what can be done, but a DS – by knowing programming – can answer way more kinds of questions and work on way more kinds of tasks.
    • Leads to reproducibility 1, scalability
    • See discussion with Hadley Wickham
  • Single/primary distinction: statistical and machine learning (ML) modeling
  • “not all DS work requires ML but ML is required to be a Data Scientist” 3
  • No DAs, just two types of DSs: “Type A” vs “Type B” (refer to Doing Data Science at Twitter) came up a few times
  • Emily Robinson brought up that “Data Scientist” is now also used as an umbrella term and specialties are specified in the title as needed 4
  • Some big tech companies like Facebook, Spotify, and some departments within Apple are moving away from having DAs to just having DSs 5
  • Practical considerations for NY/SF/Austin tech scene:
    • “DS title will need a higher salary.”
    • “You will lose talent because of the DA title. It is seen as less prestigious.”
    • “You may have to work harder for diverse pool of applicants w/ DS title.”
      • “That latter comes from one company I know who’s had a harder time getting female applicants for DS positions vs DA (when they’re fairly similar responsibilities)” 6
  • Lucas Meyer voiced support for a classic: Drew Conway’s infamous Venn diagram 7
  • A coworker of mine shared that at one of his previous employments his organization identified three data scientist personas/profiles:
    • *DS, Operations- provides data & insights for resourcing decisions through ad-hoc analyses, dashboards, defining KPIs, and A/B testing.
      • This is the role of a *Data Scientist in Product- who creates reports and dashboards for management and executives. - MP
    • *DS, Product- delivers data science *as- product (and not to be confused with Data Scientists *in- Product). These folks build predictive models, AIs, matchmaking systems.
      • In some orgs this might be an *ML Engineer- or an *AI Engineer- or just a Data Scientist? - MP
    • *DS, Research- experiments and innovates. Not everything they work on ends up in production or utilized, but they are free to be creative and take chances.
      • In some orgs this might be the Research Scientist? - MP
    • Thinking of it this way, you might envision a scenario/pipeline wherein a Research DS prototypes a new recommender system (RS) algorithm, then an Operations DS helps determine (through A/B testing and qualitative user research together with a Design/UX Researcher) whether it’s worth the costs to productionize (perhaps with the input of a Business/Financial Analyst), and then a Product DS scales the RS (possibly in collaboration with a Data Engineer) and deploys it to production. - MP

Closing thoughts

I hope for some that this is an eye-opening moment and that they now realize that there’s no single distinction everyone agrees on. Everyone is coming into it with their own backgrounds, experiences, thought processes, and ideas. None of these are wrong! So if you’re in a hiring position, please remember to be specific when writing a job description. You can’t just write “Data Analyst” or “Data Scientist” at the top and expect everyone else to share your assumptions, it’s a recipe for misunderstanding and failure.

I would like to thank everyone who responded, and especially Emily Robinson and Renee M. P. Teate. Thank you everybody for taking the time to write and in some cases discuss nuances in spun-off threads! If you want to explore all the replies yourself, here’s root.

I would also like to point out that is not even representative of how data professionals perceive these roles globally. All of the responses were from English-literate people, most (if not all) of the responses were from people living and working in U.S., and many of them are specifically people who follow me on Twitter. I know for a fact that there are so many more data professionals (data engineers have opinions on this too!) who aren’t in any of those groups. These are professionals who have their own perceptions, who operate in different cultures, under different expectations all across the world, and someone out there is probably writing a similar post within their own community.

Posted on:
May 24, 2018
5 minute read, 1001 words
See Also:
Probabilistic programming languages for statistical inference
Mostly-free resources for learning data science