What kind of topics do data scientist/engineers work on in corporates on a day to day basis
May 8, 2020
3 Comments
What kind of topics do data scientist work on in corporates. Looking for broad areas:
For ex:
compnaies like Goldman, JP MORGAN is one category
Google Microsoft, apple etc. Are one category
I would like to know from your experiences what kind of problems arise and how is stats/quants/ML relevant to these from experience working on those topics
#tech #career
comments
Data Scientist:
SAAS company — Customer churn & Retention, Product demand
Fintech — Loan defaulters, Risk Modeling,
Gaming — LTV, Customer Churn, Impact of gaming features on player behavior, playing pattern and monetization
Data Engineer is bit broad area but involves build & maintain ML pipelines, Data Pipelines, Internal tools, Data Models, Reporting layers etc etc
- propensity to do something
- churn(flipside retention)
- life time value
- recommendation
Where you can make the above bespoke to the industry for example - churn for a subscription platform could be recast to churn for a account at a bank, client using your tools or whatever.
Now specific models
- fraud detection
- document processing
- etc
There are obviously a wide range of models and use cases, but for a vast majority of the companies who are starting or part way through bring ML models the big 4 are the first to get buy in and potential bring the biggest chunk of improvement with the initial investment.
7 yoe DS/Ml within various industries.