Build a Reproducible Pricing Analysis for a DTC Skincare Brand
Overview
What this challenge is about.
You receive 24 months of order-line data (around 480,000 lines), a Shopify-style customer export, and a discount-code log. Build a Python pipeline that produces: SKU-level price-elasticity estimates using the past 18 months of natural price variation (e.g., promo periods), a cohort-revenue retention chart, and a recommended new price for each of the 12 SKUs with predicted revenue impact and a clearly-stated confidence interval. The pipeline must be re-runnable by the finance team monthly with one command.
The Brief
What you'll do, and what you'll demonstrate.
Recommend SKU-level price changes backed by elasticity estimates and cohort-impact projections, delivered as a one-command-rerun pipeline.
Earning criteria — what you'll demonstrate
- Wrangle commerce data with realistic mess (returns, partial refunds, currency)
- Estimate price elasticity from observational data and state caveats
- Build a cohort view and explain what the curve does and doesn't say
- Package the analysis as a re-runnable artifact, not a one-shot notebook
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Data Scientist
Pricing analysis with elasticity estimation and cohort retention is the bread-and-butter project portfolio of a junior data scientist at a DTC or consumer-tech company.
This challenge sharpens
- regression-modeling
- cohort-analysis
- exploratory-data-analysis
Data Engineer
Turning a notebook into a one-command rerunnable pipeline with documented inputs/outputs is the entry point to data-engineering work.
This challenge sharpens
- reproducible-analysis
- data-wrangling
- python
Applied AI Scientist
Building a defensible quantitative recommendation a Chief Financial Officer can act on mirrors applied AI work: model + business reasoning + clear communication.
This challenge sharpens
- regression-modeling
- cohort-analysis
- reproducible-analysis