Full-Time

Team Lead Data Science

Enpal

Enpal

1,001-5,000 employees

Residential solar leasing with energy ecosystem

No salary listed

Berlin, Germany

Hybrid

Hybrid work model; Berlin office (Berlin-Friedrichshain). Optional international relocation up to 30 days per year.

Category
Data & Analytics (1)
Required Skills
MLOps
Microsoft Azure
Python
Apache Spark
SQL
Machine Learning
Data Engineering
AWS
DevOps
Snowflake
Google Cloud Platform

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Requirements
  • 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 production, and to deliver outcomes through a team.
  • Fluent in English.
Responsibilities
  • 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 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.
Desired Qualifications
  • Experience with long-term forecasting (seasonal, multi-year horizons) or scenario modelling.
  • Experience with probabilistic forecasting or uncertainty quantification (e.g. conformal prediction, quantile regression, Monte Carlo simulation).
  • Experience working with weather data or numerical weather prediction models.
  • Experience scaling ML systems: managing many models in parallel, high-throughput inference, or model fleet management.
  • Experience with price forecasting or price forward curves in energy markets.

Enpal provides residential renewable energy solutions in Germany, offering solar PV, battery storage, EV charging, and heat pumps through an all-in-one model that started as a solar subscription and now includes direct purchase and financing. Its products are connected via Enpal.One, a smart energy management system that optimizes household energy use, while Enpal handles consultation, system design, installation by its own technicians, and grid connection. The differentiator is the end-to-end, flexible subscription plus a growing digital ecosystem, including Enpal.pro for installers and a Virtual Power Plant for energy trading. The goal is to speed up the switch to clean energy, lower barriers to solar adoption, and help households achieve energy independence.

Company Size

1,001-5,000

Company Stage

Debt Financing

Total Funding

$6B

Headquarters

Berlin, Germany

Founded

2017

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Simplify Jobs

Simplify's Take

What believers are saying

  • Higher electricity prices support demand for residential solar and heat pumps.[1]
  • Enpal's integrated offering expands cross-sell across solar, storage, and heating.[2]
  • Enpal.One creates future monetization beyond installation through market participation.[1]

What critics are saying

  • Competitors like 1Komma5° intensify pricing pressure and customer acquisition costs.
  • Enpal's financing-heavy model depends on continued access to cheap capital.
  • A decline in electricity prices would weaken the current demand surge.

What makes Enpal unique

  • Enpal bundles solar, storage, wallboxes, and heat pumps into one package.[2]
  • Enpal.One links homes to electricity markets as an AI-powered virtual power plant.[1]
  • Enpal offers both solar rental and direct purchase options in Germany.[2]

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Benefits

Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

0%

2 year growth

-1%
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