Team Lead Data Science; f​/m​/x

At Enpal, we are not just a company; we are a movement. As a recognized greentech unicorn and one of Europe’s fastest-growing energy companies, we’re dedicated to making solar energy accessible and effortless for homeowners across the continent. Our innovative business model for solar panels, heat pumps, home energy storage systems, and EV charging stations is redefining the residential energy market.

With our mission to empower homeowners to embrace clean energy, we are paving the way for a sustainable, decentralized energy future.

As the leading player in residential solar, Enpal is expanding its next frontier:
Enpal Energy
, our platform for managing, optimizing, and orchestrating millions of distributed energy assets. This includes building Europe’s largest Virtual Power Plant (VPP), connecting solar systems, batteries, heat pumps, and EV chargers to actively stabilize the grid and unlock new revenue streams for our customers.

Our vision is bold: to become the driving force in Europe’s transition to distributed, intelligent, and clean energy generation.

We are looking for a Team Lead Data Science to lead our Energy Data Science team, a focused team of three data scientists building and operating the forecasting systems that sit at the heart of Enpal’s energy trading operation. You will own short‑term and long‑term models, as well as risk simulations, and your team’s output will directly inform trading decisions across one of Europe’s largest residential energy communities.

This is a hands‑on leadership role (~65% technical, ~35% leadership). You are expected to stay deep in the work — modelling strategy, architecture decisions, critical code and design reviews — while leading your team and owning its outcomes.

• Research, develop, and product ionize time‑series forecasting models for energy markets (e.g. load and generation forecasts), owning the full lifecycle from data exploration and feature engineering through model training, evaluation, deployment, monitoring, and retraining.
• Contribute to our MLOps infrastructure: experiment tracking, model versioning, automated retraining pipelines, and production observability.
• Communicate forecasting model decisions and performance to C‑level leadership, and stakeholders in trading and risk management functions, connecting business understanding and impact with deep technical knowledge.
• Collaborate closely with Traders/ Portfolio Managers, Data Engineers, Software Engineers, and Product to deliver well‑engineered, reliable data science products.
• Own and evolve the team’s forecasting roadmap, including future use cases such as long‑term forecasting (1–2 year horizon), price forecasting, and probabilistic risk simulations.
• Lead, mentor, and develop a team of data scientists — including 1:1s, performance reviews, career development, and hiring decisions.
• Foster a culture of continuous learning, engineering quality, and technical growth.

• University degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
• 5+ years of industry experience in machine learning, with a strong focus on time‑series forecasting or data science in the energy sector.
• 2+ years of experience leading a data science team or multi‑person project, including formal people‑management responsibilities (performance reviews, mentoring, hiring).
• Proficiency in Python and SQL
  , with strong software engineering fundamentals: testing, code quality, version control, and CI/CD.
• Proven experience deploying, monitoring, and maintaining machine learning models in production environments.
• Hands‑on experience with MLOps practices and tooling: experiment tracking, model registries, automated retraining workflows, and CI/CD for ML.
• Practical experience with data engineering tasks: pipeline development, data quality management, and feature engineering at scale.
• Experience with cloud infrastructure (Azure, AWS, or GCP) and cloud data warehouses (Snowflake or similar).
• Experience with distributed data processing frameworks (PySpark, Dask, or similar).
• End‑to‑end ownership mindset
  : proven ability to drive projects from research through…