I suppose ideally if you do code you'd have a repository of work to show and wouldn't be expected to code so much in an interview
The first one asked me to implement a Naive-Bayes Classifier on Map Reduce from scratch (ie. no Naive-Bayes libraries), gave me real customer data & I was supposed to run the classifier & pick out the list of customers who should be sent emails based on the classifier's results. All in the course of 1 hour!
The second startup gave me a time-series signal and I was asked to figure out whenever the washing machine was running. When you have electric signals, you can decompose them into fourier harmonics, & running the washing machine leaves a distinct signature - you'd get extra harmonics if you ran the washer.
The third one asked me to flag a bunch of potential delinquent borrowers from a 30-year home loan repayment schedule. Risk prediction
All 3 were fairly code-heavy (150-200 scala loc)
If someone had told me you could datamine electrical consumption from smart meters, figure out when the customer is running his washer-dryer, and ask the customer to reschedule his laundry routine for a slightly smaller bill so the utility company gets a smoother duty cycle throughout the day, and there was a business model in that, I'd be flabbergasted. And yet, it exists!
This grocery startup that employs essentially portfolio management theory to optimize cost given calorie consumption is not entirely fiction either.
There's a company that datamines historical MLS data joined with facebook data & predicts which customers are potential home buyers because they are having a "life-event" ( usually birth of kid, so need extra room ) & realtors cold-call these potential home buyers.
There's an airline that datamines flight paths of every pilot on its payroll ( essentially (x,y,z,t) coordinates at time t) and flags the dangerous/risky pilots ie. the pilot who is prone to choose those flight paths that bring him very close to another airline, thus creating potential collisions between the airplanes.
I've seen demos of all of the above & more. We live in very, very interesting times!