Data Science Team Lead

Requirements

• Minimum of five years of experience with time-series forecasting
• Graduate degree in Computer Science, Data Science, Mathematics, Engineering, or a related field, or related years of experience
• Strong Python for data science (NumPy, Pandas, scikit-learn, gradient boosting frameworks).
• Proven experience managing direct reports in a data science, machine learning, analytics, forecasting, or software engineering environment.
• Experience leading a technical team through delivery of production-grade models, data products, analytics systems, or SaaS capabilities.
• Hands-on experience applying AI/ML techniques to time-series problems (e.g., forecasting, anomaly detection, probabilistic prediction), ideally in the energy industry.
• Proficiency in backend languages, especially C#
• Strong forecasting and time-series fundamentals, including experimental design and model validation.
• Familiarity with Agile development methodologies.
• Excellent communication and teamwork skills. Passion for sharing knowledge with the rest of your team, building shared tooling, and directly helping customers.
• Self-directed and comfortable supporting the needs of multiple teams, systems, and products.
• Continual learner, engaged in professional development.
• Preferred Qualifications & Key Competencies
• Fortran is strongly preferred; experience collaborating across Python/Fortran boundaries is a valuable plus.
• Demonstrat

Additional Information

Join the Market Leader in Electric Power Data and Analytics Solutions The electrical grid is the largest and most complicated machine ever built. Yes Energy's industry-leading electric power trading analytics software provides real-time visibility into the massive amount of data generated by the North American electrical grid daily. Our unique and innovative view of the data informs real-time trading decisions and mid-to-long-term investment decisions that keep utility prices low, support the energy transition, and keep the grid running. It's both challenging work and work with a purpose. Be a part of our successful, growing business during international transformation. Position Summary This is a team lead position on the Forecasting Data Science team, combining hands-on data science leadership with direct people management responsibility. This role is responsible for managing a growing team, setting priorities, supporting performance management, and driving execution across cross-functional stakeholders. The work spans python-based libraries and pipelines for data extraction, feature engineering, supervised learning, and robust handoff into operational systems. Fortran experience is not required, but it would be a strong plus when interfacing with legacy algorithms. Fortran experience is strongly preferred since it would be beneficial when interfacing with legacy algorithms. Success in this role requires both strong applied forecasting and machine learning expertise and proven people-management skills, including experience leading direct reports, providing feedback and aligning individual work with team and business priorities. Position Details Salary range: Net 20.000 - 25.000 RON/month Location: Bucharest, Romania Full-time Hybrid - 2 days in the office Reporting to: Engineering Manager Primary Responsibilities Research, develop, and improve demand and renewable forecasting and predictive models for current use cases and new market opportunities. Balance hands-on technical contribution with people leadership, ensuring the team delivers high-quality forecasting models, reliable operations, and scalable model-development practices. Apply advanced statistical, machine learning, and time-series methods to energy and weather-driven datasets; ideally, bring direct time-series experience in the energy industry. Design, train, and evaluate AI/ML models for time-series forecasting in the energy market and weather-driven applications, including feature engineering, model selection, and performance monitoring. Build and evolve evaluation frameworks and experiments to validate model accuracy, stability, and business impact over time. Collaborate with Forecast Analysts, Product, and Engineering to make models practical to train, tune, monitor, and reproduce in a SaaS delivery model. Lead technical direction for modeling initiatives, including approach selection, quality standards, and trade-off decisions. Mentor peers and analysts; raise the quality of experiments, code, documentation, and model operations. Partner with Platform/Engineering on scalable implementation patterns for CI/CD, configuration, containers, and integration. Drive cross-team initiatives that expand forecasting capability across geographies, asset types, and forecast horizons. Communicate assumptions, findings, uncertainty, and trade-offs clearly to technical and non-technical stakeholders. Stay current on power market structure, regulatory changes, and technology shifts that influence forecasting requirements.

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