Scope a Demand-Forecasting Model with Operations Stakeholders
Overview
What this challenge is about.
You receive recorded interview transcripts (or summary notes) for the three personas, plus a sample of the historical sales data. Map each stakeholder's pain to candidate ML problems (e.g., per-SKU daily forecasts vs. category-level weekly forecasts), score them on impact vs. feasibility (high/medium/low), and pick one to write up as the V1 scope. Define the target variable, the unit of prediction, the success metric (e.g., weighted MAPE), the operational decision the forecast supports, and the explicit non-goals. Deliver the brief plus a 6-item backlog the data team can size next sprint.
The Brief
What you'll do, and what you'll demonstrate.
Translate operations-team pain into a tightly scoped, measurable ML forecasting problem the data team can start building.
Earning criteria — what you'll demonstrate
- Translate vague stakeholder pain into a measurable ML problem statement
- Choose evaluation metrics that map to a real operational decision
- Document explicit non-goals to avoid scope creep
- Apply a lightweight prioritization framework to a candidate backlog
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.
AI Product Manager
Stakeholder discovery, ML problem scoping, and metric-to-decision mapping are the daily craft of an AI PM at any operations-heavy company.
This challenge sharpens
- stakeholder-framing
- ml-problem-scoping
- prioritization
Applied AI Scientist
Choosing the right metric for the operational decision is what separates applied AI work from textbook ML and is graded in every applied-AI interview loop.
This challenge sharpens
- metric-design
- ml-problem-scoping
- stakeholder-framing
AI Solutions Architect
Producing a sized backlog grounded in stakeholder pain is the entry deliverable for solutions architects scoping ML engagements at consulting firms.
This challenge sharpens
- requirements-writing
- prioritization
- ml-problem-scoping