Data Scientist / ML Engineer (F/M/D)

About the role

The Ignitis Group shared service center is looking for a Data Scientist / ML Engineer to join its AI Adoption and Innovation team. You will develop data-driven and machine-learning solutions for renewable energy generation, forecasting, asset performance and other energy-sector challenges, working closely with renewable energy, commercial, data and engineering teams.

Key responsibilities

• Build forecasting and machine-learning solutions for wind, solar, battery energy storage and other renewable energy use cases
• Develop energy-generation forecasting models using weather forecasts, historical production, operational and external data
• Identify anomalies, performance deviations, equipment risks and potential maintenance needs in energy assets
• Explore optimisation opportunities for generation, storage, asset operations and commercial decisions
• Collect, clean, transform and validate large volumes of structured, time-series, geospatial and unstructured data
• Perform exploratory data analysis and communicate findings to technical and business stakeholders
• Create prototypes and MVPs and turn successful models into maintainable production solutions
• Collaborate with renewable energy, commercial, engineering, IT, data and AI teams across Ignitis Group

Required profile

• Strong practical experience in data science, machine learning, applied statistics or predictive analytics
• Experience with time-series data and forecasting, anomaly detection, optimisation or predictive models
• Understanding of model validation, feature engineering, backtesting, performance metrics and error analysis
• Ability to explain model results, assumptions and limitations to non-technical stakeholders
• Strong English communication skills
• Advantage: experience with renewable energy, electricity markets, wind or solar generation, BESS, grid, weather or SCADA data, and with weather forecasting, geospatial, GIS or satellite imagery data

Required skills

• Python with libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, PyTorch or TensorFlow
• Data preparation and reproducible data-processing and modelling pipelines
• Azure, Google Cloud, AWS, Databricks, Microsoft Fabric or comparable platforms

What we offer

• Hybrid work model (3+2) and flexible working hours
• Additional paid days for health improvement, short-term illness, volunteering and accumulated work experience
• Choice of benefits such as pension accumulation or health insurance
• Learning and qualification programmes, mentorship and internal career opportunities