Team Lead Data Science (f/m/x)

Team Lead Data Science (f/m/x)

2.82.8 von 5 Sternen

Berlin

Link: (CompanyWebsite)

Mitarbeiteranzahl: 51 bis 200 (CompanyEmployee)

Branche: Energie- & Versorgungswirtschaft (CompanyIndustry)

Stellenbeschreibung

Anstellungsart

Vollzeit

Arbeitsort

Berlin

Leistungen

Aus der vollständigen Stellenbeschreibung

Firmenevents

Vollständige Stellenbeschreibung

At Enpal, we are not just a company; we are a movement. As a recognized greentech unicorn and one of Europes fastest-growing energy companies, were 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 Europes 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 Europes transition to distributed, intelligent, and clean energy generation.

About the role

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 Enpals energy trading operation. You will own short-term and long-term models, as well as risk simulations, and your teams output will directly inform trading decisions across one of Europes 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.

What youll do

Research, develop, and productionize 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 teams 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.

What youll bring

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.