Grow your data science skills at posit::conf(2024)

August 12th-14th in Seattle

20 Jan 2022

Data science team culture

Asmae Toumi

Director of Analytics at PursueCare
Data engineers vs scientists vs analysts – not sure?
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Episode notes

We were recently joined by Asmae Toumi, Director of Analytics at PursueCare, to discuss the most important things going on in data science leadership.


There was a great discussion during this session starting from the question, “Is it ok to say that we are data analysts and data scientists interchangeably?”


Javier: Some of the best data scientists have the title of financial modeler or senior actuary. Some of the best programmers and package developers I know, have the title of data analyst. I think it depends on the organization. We have these guidelines that are more of an ideological function of a data scientist vs data analyst but I think in practice it’s all over the place.


Asmae: Jacqueline Nolis and Emily Robinson have good thoughts about this: (Short rules from the book I saw: An analyst creates dashboards and reports that deliver data. A decision scientist creates analyses that produce recommendations.)


Mike: I think to anyone that is OUTSIDE of Data Science / Data Analytics, the distinction is not worth discussion. If you want funding you’re a Data Scientist. If you HAVE funding it doesn’t matter.


Frank: No right answer (DS vs. DA)… only different ways to think about it at different times in different contexts.


Jordan: I’ve usually been working in a “Science” (science/research) domain at companies. To this end, I’ve sort of been the “Data” Scientist, maybe not “Data Scientist”


*What about business intelligence?*


Asmae: I use data analyst and data scientist interchangeably. I think there is a meaningful difference there between a business analyst and a data scientist.


*What about data engineering?*


Asmae: We have 3 data engineers. Data scientists all want a data engineer. As a healthcare company, it’s complex to deliver the care that we do. With that complexity comes interfacing with different data vendors – different data sources so it’s really important to invest in data engineering so the reconciliation of that data across the many vendors is ensured. Data quality is also super important.


Bryan: Data engineering is the foundation of it all — need a solid data engineer to do anything


Falak: Data scientists understand the distribution of the data and figure out what class of algorithms would be suitable to handle a particular problem. Data engineers can solve the optimization problem.


Other resources shared during the hangout:

Allissa: An NIH prize challenge on maternal health that may be of interest to some –

Bala: Top 10 Roles for your Data Science Team:

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