Data Scientist II

This role is on the Forecasting Data Science team and combines research with productized model delivery in a forecasting SaaS environment. Based in our Bucharest office, you will partner closely with Forecast Analysts in an operational capacity—managing daily model configurations, runtime monitoring, and exception handling—alongside Product and Engineering teams. You will ensure models are accurate, reliable, and usable in production-grade services, bridging the gap between data exploration and robust operational systems.

Responsibilities

Build and scale Python-based libraries and pipelines for data extraction, feature engineering, and supervised learning; Support daily forecasting operations, including runtime monitoring, exception handling, and model configuration; Collaborate with Engineering to ensure robust handoff of research models into production systems; Independently tackle moderately complex problems using data patterns to pinpoint root causes; Design and implement scalable, well-reasoned solutions that consider the business impact on power market participants; Participate in research and development to improve model accuracy in a forecasting SaaS environment; Communicate technical workflows and performance metrics clearly to stakeholders and cross-functional partners; Document model methodologies and operational workflows to maintain high standards of system reliability.

Requirements

3+ years of professional experience in data science, specifically focused on predictive modeling or forecasting; Master’s degree in a quantitative field (e.g., Statistics, Mathematics, Data Science, or Computer Science); Advanced proficiency in Python and SQL for data manipulation and model development; Demonstrated experience with machine learning frameworks and production-grade data pipelines; Ability to work effectively in an operational capacity, managing real-time model performance and troubleshooting.

Nice to have

Problem Solving: Independently solves moderately complex problems by pinpointing root causes and proposing scalable, well-reasoned solutions; Systems Thinking: Takes a holistic view of how data, models, and technology interact within the forecasting ecosystem; Econometrics & Statistics: Applies rigorous mathematical techniques to analyze economic data and generate precise forecasts; Artificial Intelligence: Skill in selecting and applying appropriate AI models to enhance decision-making and create value; Effective Communication: Translates complex data findings into actionable insights for both technical and non-technical audiences; Prioritization: Balances the demands of daily operations with long-term research and development goals; Programming Languages: Familiarity with programming languages such as Fortran and C# is considered a plus; Energy Industry Knowledge: (Preferred) Experience with power markets or grid infrastructure is highly beneficial.

Benefits

Competitive salaries; Real bonuses; Private medical insurance; Wellness/gym benefits; Flexible vacation; Flexible work schedules; Investment in formal and informal professional development