Data Scientist II at Yes Energy

Bucharest, , Romania -

Full Time

Start Date

Immediate

Expiry Date

11 Aug, 26

Salary

230000.0

Posted On

13 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Predictive Modeling, Forecasting, Machine Learning, Data Pipelines, Feature Engineering, Supervised Learning, Econometrics, Statistics, AI Models, Problem Solving, Systems Thinking, Technical Communication, Fortran, C#

Industry

Software Development

Description

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 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. Position Details Salary Range: RON 140,000 - RON 230,000 Location: Bucharest, Romania Full-time Hybrid - 2 days in the office Reporting to: Engineering Manager Primary 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. Minimum Qualifications 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. Preferred Qualifications & Key Competencies 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. 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 1,000 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 we’re 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. We’re 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 is also very competitive, including medical insurance, a 401 (k) Plan with matching, flexible vacation, and flexible work schedules. Yes Energy encourages and funds investment in both formal and informal professional development. At Yes Energy, we are dedicated to building a diverse, inclusive, and authentic workplace. If you’re excited about this role but your experience doesn’t perfectly align with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. In accordance with Colorado law, the range provided is Yes Energy’s reasonable estimate of the base compensation for this role. The actual amount may be higher or lower based on non-discriminatory factors such as location, experience, knowledge, skills, and abilities. Yes Energy provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. In addition to federal law requirements, Yes Energy complies with applicable state and local laws governing nondiscrimination in employment in every location where the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Responsibilities

Build and scale Python-based libraries and pipelines for data extraction and supervised learning within a forecasting SaaS environment. Support daily operations through runtime monitoring, exception handling, and collaborating with engineering to move research models into production.