Hi, got contacted for a DS interview by BCG Gamma. How much of the interview process revolves around machine learning/advanced modeling vs traditional cases? Best ways to prepare? Thanks!
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Case Interview : client is facing a problem and we want to fix it w/ my experience, Design experiment .
Context: fast moving consumer retailer: eg 7/11 or target.
Local promotions for items on sale w/ discount
Client is trying to evaluate the benefits of coordinated national marketing
What is the optimal discount that should pursue
All promotions at the client run weekly, meaning they start Monday and end on Sunday
No canablization across products, all product sales are independent
Price elasticity is linear. If you raise the price, demand doesn’t really drop.
Question: Exploratory w/ Linear Regression to show price elasticity for each different product. Predict price elasticity.
The first one was to formulate a linear regression model to predict units sold given price. Then you can use coefficient on the predictor price as an indicator of price elasticity. Meaning for a unit increase of price you’ll see w increase in Sales. Where w will probably be negative or very small positive. It would be small positive if the item is inelastic to price, for example, gas. For example, if gas price increases, volume will not decease as much as luxury products for example.
He then posed a harder problem which I wasn’t able to solve, but given that we have a model for each product to predict units sold, and a budget for a whole store, how much should be price each item?
I formulated it as a linear combination of prices and elasticity: $Product1 * w1 + $product2 * w2 + … + $product_i * wi <= BUDGET. How do you choose the prices now? This is a constrained optimization problem.
THEN, he posed a very interesting question, albeit a bit flawed for the interview context, about RF regression. We take that same data for the linear model and fit it using a RF regression. He then said the client complained that the fit isn’t monotonic, or in other words, it has a flat spots and this is wrong. WHY?
This is b/c the DT fitting routine recursively splits the decision space. In this case, ti just finds the median/mean of the points at the split and draws a straight line. regtree2.png . Therefore if you don’t have enough estimators in your RF, you will see the flat spots!
In general, a Gamma data scientist spends ~70% of their time coding large-scale ML pipelines and algorithms, and the rest on the strategic and business aspects.
Or are all interviews case interviews with focus on conceptual understanding?