Data Scientist (Time Series Forecasting)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Scientist (Time Series Forecasting) based in Saudi Arabia. Join a data-driven team developing advanced forecasting solutions that power a live energy trading platform. In this role, you will build and optimize time series models to predict weather conditions, energy market prices, and electricity grid supply and demand. Your work will directly support data-informed trading decisions by transforming analytical models into reliable, production-ready code. You will collaborate in a fully remote, distributed environment where quantitative expertise, technical excellence, and practical problem-solving are highly valued. Leveraging Python, SQL, and modern AI-powered coding tools, you will help bridge the gap between sophisticated forecasting techniques and real-world business applications. This opportunity is ideal for a hands-on data scientist who enjoys solving complex prediction challenges and delivering measurable impact in the evolving energy sector. 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. How Jobgether Works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Show more Show less

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.

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.