Data Science Team Lead

Join the Market Leader in Electric Power Data and Analytics Solutions

The electrical grid is the largest and most complicated machine ever built. Yes Energys 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. Its 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.

Minimum Qualifications

• 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.
• Demonstrated ability to lead technical initiatives and mentor others in modeling and applied ML engineering practices.
• Experience aligning ML design with system constraints and multi-stakeholder needs in a SaaS product environment.
• Strength in shaping how experimentation packaging and deployment practices fit together for dependable delivery.
• Experience with modern AI approaches for time-series modeling (e.g. gradient boosting deep learning sequence models hybrid/statistical-ML methods) in production environments.
• Advanced Analytical & Problem Solving Applies deep expertise in statistical modeling machine learning and time-series forecasting to solve complex ambiguous problems and drive impactful outcomes.
• Technical Execution & Data Science Expertise Demonstrates strong proficiency in Python-based data science tools model development feature engineering and scalable ML practices within production environments.
• Data Management & Quality Ensures high standards for data integrity validation and reproducibility across datasets models and forecasting pipelines.
• Systems Thinking Understands how models interact within broader systems including data pipelines SaaS delivery architecture and operational forecasting workflows.
• Research & Innovation Continuously explores new methodologies tools and approaches to improve forecasting accuracy scalability and business impact.
• Energy Industry & Market Knowledge Applies knowledge of energy markets weather drivers and grid dynamics to inform model development and forecasting strategies.
• Stakeholder Focus Partners effectively with Forecast Analysts Product and Engineering to ensure models meet real-world use cases and customer needs.
• Effective Communication Clearly communicates technical concepts model assumptions trade-offs and uncertainty to both technical and non-technical audiences.
• Strategic Thinking Contributes to long-term forecasting strategy including expanding capabilities across markets asset types and forecast horizons.
• Ownership & Initiative Takes responsibility for model performance operational reliability and continuous improvement in a production environment.
• Team Focus & Knowledge Sharing Collaborates across teams and mentors peers to elevate modeling practices experimentation quality and technical standards.
• Prioritization & Operational Efficiency Balances research development and operational support work while driving efficient scalable processes.

At Yes Energy we value connecting directly with candidates. We kindly ask that third-party recruiters and agencies not submit resumes as we are not open to external recruiting partnerships.

ABOUT YES ENERGY

Overview

Yes Energy delivers real-time market data and electric power trading decision solutions. Over 1000 market participants use Yes Energy solutions daily. The business is a leader in all aspects of information content collection and management developing and delivering data and market analytics solutions. Since its inception in 2008 Yes Energy has become a trusted and respected supplier of innovative and reliable solutions focused on the needs of power market analysts traders and trade managers. Yes Energy has a team of over 350 amazing professionals in Boulder CO (HQ); Boston MA; Chicago IL; Glendora CA; Richmond VA; London United Kingdom; Auckland New Zealand; and Bucharest Romania.

Culture

Yes Energy has been named one of the Best Places to Work in Colorado and we have the culture to prove it. At Yes Energy we care about saying Yes to customers. We like to listen learn and develop our solutions in line with their needs. We think about customers as business partners and when we help them be more successful we are more successful too.

Around the office our culture is driven by some pretty fundamental values that were proud of:

• We love innovation and solving tough challenges;
• We are high standards people who combine passion and pride with hard work and rewards of all kinds-- in an ethic that is consistent across the company.
• Were team-focused with a flat hierarchy-- we work in small teams on well-defined projects that directly impact the success of the business;
• We play to the strengths and experience of each person while each of us also works along a continuum of roles adjacent to our focus area. This presents the challenge of maintaining a broad set of skills as well as an opportunity to learn and contribute in many ways.
• We are constantly growing. Professional development happens every day and every year.

Compensation and Benefits

We offer highly competitive salaries and real bonuses that are achievable and that you can impact. Our benefits package includes private medical insurance wellness/gym benefits flexible vacation and flexible work schedules. Yes Energy encourages and funds investment in both formal and informal professional development.
Yes Energy provides equal employment opportunities to all employees and applicants without regard to race color religion sex national origin age disability genetics or any other protected status. This policy applies to all terms and conditions of employment including recruiting hiring placement promotion termination compensation and training.

Employment Type : Full Time
Experience: years
Vacancy: 1