ML Engineer
Energy Bay’s ML engineer forecasts electricity and ancillary-service prices. The role turns those predictions into battery bidding and dispatch decisions.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Build probabilistic price forecasts.
- Improve battery-dispatch optimisation.
- Monitor and retrain production models.
What they ask for
Required
- Python
- SQL
- Time-series forecasting
- Production ML
- Data pipelines
Preferred
- NEM and FCAS knowledge
- MILP
- MLOps
- Education
- Relevant quantitative bachelor’s or master’s degree required.
- Experience
- Three to five years in ML, data science or quantitative analytics.
- Languages and tools
- Markets
- commodities
How to prepare
- Forecast price spikes probabilistically.
- Optimise battery dispatch under constraints.
- Compare realised revenue with forecast-based decisions.
What to study
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Energy Bay Funds Management’s site before applying: requirements, location and pay can change after we check them.
View the posting on Energy Bay Funds Management’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Energy Bay Funds Management and doesn’t handle applications.