Take-Home Data Challenges

Many quant roles include a take-home challenge: a dataset, a loosely worded prompt like "find something interesting" or "predict this target," and a few days to submit something. The trap is treating this like a school assignment where a higher accuracy score wins — in practice, the firm is reading the deliverable for judgment, not just for a working model, and a candidate who tunes a model to squeeze out an extra percentage point of accuracy while ignoring the data's obvious problems has misread the assignment.

What actually gets evaluated

Before any modeling, spend real time on the data itself: are there missing values, and do they look missing at random or missing for a reason that matters (see Missing Data and Imputation)? Is there any leakage — a column that wouldn't actually be known at prediction time? Is the data point-in-time correct, or could it be using information that wasn't available when a real decision would have had to be made? Reviewers who have seen hundreds of these submissions can tell within minutes whether a candidate checked for these issues, because the ones who didn't usually report a suspiciously good result.

The write-up matters as much as the code. A short, clear explanation of what you tried, what you rejected and why, and what you'd do next with more time or better data reads as far stronger than a long notebook with no narrative — because it's the write-up, not the raw code, that shows the thinking a colleague would actually rely on day to day.

Example

Given a dataset to predict which trades will be profitable, a strong submission opens by flagging that one feature (the trade's realized holding period) wouldn't have been known at the time a decision needed to be made, excludes it as leakage even though including it would have boosted the reported accuracy, and says so explicitly in the write-up: "I excluded holding period as a feature because it's only known after the trade closes, which makes it unusable for a live prediction — this cost about 8 points of accuracy but the resulting model is the one that could actually be deployed."