Take-Home Projects
0 of 1 free take-home accessed
A data science take-home/portfolio exercise about using customer features V1-V7 to decide who should receive a promotion, optimizing IRR and NIR. Submission includes explicit requirements, train/test CSVs, a notebook analysis and strategy implementation, a written report summarizing results, and a scoring script for evaluation.
StarbucksUber Data Scientist interview take-home with two parts: SQL queries on a provided schema and predictive analysis/modeling of driver signup conversion using a supplied CSV. Submission includes the prompt, dataset, notebook solution for Part 2, PDF export of notebook, and a written answers PDF including SQL answers and Part 2 summary.
UberInstructions ask for three parts: a simple housing price model, top 10 3-grams for a Wikipedia article, and a memoizer function. Submission contains the prompt PDF, a large CSV of housing transaction data, and Python code (`house_knn.py`) that builds a simple nearest-neighbors home price predictor using prior sales and median relative absolute error. A second Python file (`getkclosest.py`) looks like auxiliary/scratch code related to nearest-neighbor logic rather than a complete response to Parts 2 or 3.
Opendoor.ComMachine learning take-home to predict total delivery duration from historical DoorDash order data and build an application that scores new deliveries from JSON input and outputs predictions. Submission contains the prompt PDF, historical/train and prediction datasets, data description, a candidate summary write-up, and a README with run instructions referencing missing code assets.
DoordashOne Airbnb analytics take-home about monitoring and improving the guest-host matching and booking funnel in Rio de Janeiro. Includes the prompt, three source datasets, and a completed presentation/report with recommendations and appended R code.
Airbnb