Data Scientist (Time Series Forecasting)

Accountabilities:: Time Series Forecasting: Design, develop, and refine forecasting models for weather conditions, energy market prices, and electricity grid supply and demand. Production Model Development: Translate forecasting algorithms and analytical models into robust, scalable, production-ready code that integrates with and powers a live energy trading platform. Data Analysis & Model Optimization: Analyze relevant datasets, identify patterns and trends, and improve forecasting accuracy to support reliable, data-driven trading decisions. AI-Assisted Development: Incorporate agentic coding tools into daily development workflows to accelerate implementation, improve productivity, and support efficient software development. Technical Integration: Work with data pipelines, analytical systems, and production environments to ensure forecasting solutions are reliable, maintainable, and suitable for operational use. Cross-Functional Collaboration: Communicate technical findings, explain forecasting approaches, and collaborate effectively with distributed teams to align model development with business and platform requirements. Requirements: Educational Background: Bachelor's degree or higher in Mathematics, Econometrics, Physics, Engineering, or another closely related quantitative discipline. Professional Experience: At least three years of relevant professional experience in data science, quantitative analysis, forecasting, or a related field. Time Series Forecasting Expertise: Demonstrated experience developing and applying time series forecasting models, with the ability to translate quantitative methods into practical solutions. Programming Skills: Strong proficiency in Python and SQL for data analysis, model development, and production implementation. Experience with Go is considered an advantage. Agentic Coding Tools: Regular, hands-on use of agentic coding tools as part of your daily development workflow, with the ability to integrate AI-assisted techniques effectively into software development. Communication Skills: Excellent English communication skills, including the ability to explain technical concepts clearly and collaborate with stakeholders in a remote, international environment. Analytical & Problem-Solving Abilities: Strong quantitative reasoning, attention to detail, and a practical approach to addressing complex forecasting challenges and improving model performance. Preferred Industry Knowledge: Experience working with energy markets, electricity trading, power grid data, or weather datasets is a plus. Benefits: Fully Remote Work: Work remotely from anywhere in a distributed team environment. International Collaboration: Opportunity to contribute to a global energy trading platform and collaborate with professionals across different locations. Meaningful Technical Impact: Develop forecasting solutions that support real-world energy market decisions and operational trading activities. Advanced Data Science Challenges: Work on practical applications of time series forecasting involving weather, market prices, and electricity supply and demand. Modern Development Practices: Use agentic coding tools and contemporary development workflows to improve productivity and streamline model implementation. Professional Growth: Strengthen your expertise in quantitative modeling, production-grade data science, and applied forecasting in the energy sector.