DATA SCIENTIST / ML ENGINEER (F/M/D)

We are currently looking for a Data Scientist / ML Engineer to join the AI Adoption and Innovation team at the Ignitis Group shared service center. In this role, you will develop data-driven and machine-learning solutions for renewable energy generation, forecasting, asset performance, and other energy-sector challenges. You will work closely with renewable energy, commercial, data, and engineering teams to explore complex datasets, build predictive models, and turn experimental solutions into reliable analytical products.
If you want to apply data science and machine learning to real energy-sector problems, we would be happy to welcome you to our team!
Take YOUR part in #EnergySmart!
You will contribute to secure and green energy ecosystem creation by:
Developing forecasting and machine-learning solutions for wind, solar, battery energy storage, and other renewable energy use cases
Building models for energy generation forecasting using weather forecasts, historical production, operational, and external data
Identifying anomalies, performance deviations, equipment risks, and potential maintenance needs in energy assets
Exploring optimisation opportunities for energy generation, storage, asset operations, and commercial decision-making
Collecting, cleaning, transforming, and validating large volumes of structured, time-series, geospatial, and unstructured data
Performing exploratory data analysis and communicating findings to technical and business stakeholders
Creating prototypes and MVPs and developing successful models into maintainable production solutions
Collaborating with renewable energy, commercial, engineering, IT, data, and AI teams across Ignitis Group
Strong practical experience in data science, machine learning, applied statistics, or predictive analytics
Strong Python programming skills and experience with libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or equivalent tools
Experience working with time-series data and developing forecasting, anomaly-detection, optimisation, or predictive models
Understanding of model validation, feature engineering, backtesting, performance metrics, and error analysis
Experience preparing data and developing reproducible data-processing and modelling pipelines
Experience with Azure, Google Cloud, AWS, Databricks, Microsoft Fabric, or comparable data and machine-learning platforms
Ability to explain model results, assumptions, and limitations to non-technical stakeholders
Ability to work independently, structure an ambiguous analytical problem, and develop it from exploration to a working solution
Strong English communication skills
Would be an advantage:
Experience working with renewable energy, electricity markets, wind or solar generation, BESS, grid, weather, or SCADA data
Experience with weather forecasting datasets, geospatial data, GIS, satellite imagery, or remote-sensing data

Employment type
Contract

Work arrangement
Hybrid