Data Scientist - Time Series Forecasting

About the role

A partner company is recruiting a Data Scientist focused on time series forecasting, based in Estonia. The role builds and optimizes models that predict weather conditions, energy market prices and electricity grid supply and demand, supporting data-informed trading decisions on a live energy trading platform. It is a fully remote position in a distributed team.

Key responsibilities

• Design, develop and refine forecasting models for weather, energy prices and grid supply and demand.
• Translate forecasting algorithms into robust, scalable, production-ready code that powers the live trading platform.
• Analyze datasets, identify patterns and trends and improve forecasting accuracy.
• Incorporate agentic coding tools into daily development workflows.
• Work with data pipelines, analytical systems and production environments so solutions are reliable and maintainable.
• Communicate technical findings and collaborate with distributed teams to align models with business needs.

Required profile

• Bachelor's degree or higher in Mathematics, Econometrics, Physics, Engineering or a closely related quantitative discipline.
• At least three years of professional experience in data science, quantitative analysis, forecasting or a related field.
• Demonstrated experience building and applying time series forecasting models.
• Excellent English, able to explain technical concepts and work with stakeholders remotely.
• Strong quantitative reasoning, attention to detail and practical problem-solving.
• Preferred: experience with energy markets, electricity trading, power grid data or weather datasets.

Required skills

• Python and SQL for data analysis, model development and production implementation.
• Experience with agentic coding tools.
• Go is considered an advantage.

What we offer

• Fully remote work in a distributed team.
• Collaboration on a global energy trading platform.
• Meaningful technical impact on real-world energy market decisions.
• Professional growth in quantitative modeling and applied forecasting.