Euristiq

Join us in building advanced forecasting and optimization capabilities for an energy trading company participating in electricity markets across multiple regions. We are looking for a Data Scientist to own the development of predictive models that directly support our trading team's market decisions. 

This is the first dedicated data science hire on the project. You will establish modeling standards, define best practices, and deliver the work yourself — across the full data science lifecycle: from time series forecasting and price prediction to battery optimization and model governance. The role requires strong technical depth and the ability to engage directly with business stakeholders in a domain where the problems are genuinely hard.

Key Responsibilities

• 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

Requirements

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 regime changes, proper train/validation/test splitting, and avoiding data leakage.

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.

Nice to Have

• You have worked in or alongside the electricity sector and understand the data and dynamics that drive it
• Experience with graph-based machine learning or graph neural networks (GNNs)
• Experience with probabilistic forecasting (prediction intervals, quantile regression, conformal prediction)
• Exposure to real-time or near-real-time inference pipelines
• Familiarity with weather data integration for load and renewable forecasting
• Familiarity with battery storage optimization or operations research methods for energy asset dispatch