Data Scientist — energy trading forecasting

HIRETOP

Що робити

Design, build, and validate time-series forecasting models for facility load, market demand, and electricity price signals
Select and tune appropriate modeling approaches (statistical, ML, and deep learning) based on the forecasting horizon, data availability, and accuracy requirements
Develop and maintain ML pipelines on the Databricks platform using PySpark, MLflow, and Delta Lake
Partner with domain experts to incorporate market structures, tariff rules, and grid dynamics into model features and evaluation frameworks
Communicate model performance, assumptions, and limitations to both technical and non-technical stakeholders

1. Time-Series Forecasting Expertise You have hands-on experience with a broad range of forecasting methods and know when to reach for each one. This includes classical statistical approaches, gradient boosting methods, and modern deep learning architectures. You understand the practical challenges of time-series work: feature engineering with lags and rolling windows, handling seasonality and ...
2. Databricks Platform Proficiency You are comfortable working end-to-end in Databricks — from exploratory analysis in notebooks to productionizing models via MLflow tracking, model registry, and scheduled jobs or workflows. Experience with Delta Lake for versioned, audit-friendly data pipelines is a strong asset. Familiarity with Unity Catalog and Databricks Feature Store is a plus.
English — B2+
Nice to Have
You have worked in or alongside the electricity sector and understand the data and dynamics that drive it

Знайдено через trackr.help/jobs · Канал: @trackrhelp · Бот для персональних сповіщень: @trackrhelpBot