Overview
What this challenge is about.
Forecast Energy Demand for a Nordic Renewable Utility. Advanced challenge in code. Writing production code that solves real engineering problems, earn a bloc...
The Brief
What you'll do, and what you'll demonstrate.
Beat the incumbent peak-hours demand forecast by 5 percent on mean pinball loss with a probabilistic, weather-aware model.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Build a probabilistic time-series forecaster (not just point estimates)
- Apply walk-forward backtesting correctly on long horizons
- Engineer weather and calendar features that genuinely help
- Communicate model uncertainty to a trading-floor audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Applied Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Applied Machine Learning 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.
- Time Series Forecasting
Apply time series forecasting to solve real industry problems and demonstrate production-level capability.
- Probabilistic Modeling
Apply probabilistic modeling to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied AI Scientist
Probabilistic forecasting with a financial-impact framing is exactly the applied AI scientist role at any utility, trading, or grid-services company.
This challenge sharpens
- time-series-forecasting
- probabilistic-modeling
- model-evaluation
Machine Learning Engineer
Production-ready training + inference + monitoring spec is the MLE deliverable for any forecast that touches money.
This challenge sharpens
- ml-pipelines
- feature-engineering
- model-evaluation
Data Scientist
Walk-forward backtests, reliability diagrams, and impact translation are core data-scientist disciplines.
This challenge sharpens
- time-series-forecasting
- probabilistic-modeling
- feature-engineering