Build a Reproducible Pricing Analysis for a DTC Skincare Brand
Overview
What this challenge is about.
Build a Python pipeline to estimate SKU price elasticity and recommend new prices for a DTC skincare brand. Get a verifiable certificate.
The scenario
The brand (around 70 staff, sells in France, Germany, and Spain) is gross-margin-positive but under pressure from rising ingredient costs; a botched price move could break a fragile cohort-retention curve.
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.
Applied Data Analysis and Practical Data Science
Master · Data Engineering
Strong alignment
This challenge maps to Applied Data Analysis and Practical Data Science at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Data Wrangling
Apply data wrangling to solve real industry problems and demonstrate production-level capability.
- Exploratory Data Analysis
Apply exploratory data analysis to solve real industry problems and demonstrate production-level capability.
- Cohort Analysis
Apply cohort analysis to solve real industry problems and demonstrate production-level capability.
- Regression Modeling
Apply regression modeling to solve real industry problems and demonstrate production-level capability.
- Reproducible Analysis
Apply reproducible analysis to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
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