Data Scientist (Time Series Forecasting)

Jobgether; Turkey; Remote; TRY 350,000 - 550,000; Full time

Benefits offered by this job

Fully remote

Global collaboration

Growth opportunities

Job summary

Jobgether in Turkey is seeking a Data Scientist (Time Series Forecasting) to join a fully remote, distributed team. You will design forecasting models for weather, energy prices, and grid supply and demand, and translate them into reliable production code powering a live energy trading platform.

The role emphasizes hands-on development, use of Python and SQL, and collaboration with cross-functional teams in a global energy trading environment.

Qualifications

• Bachelor's degree or higher in a quantitative field.
• At least three years in data science, forecasting, or related roles.
• Proficient in Python and SQL; Go is a plus.
• Experience with time series forecasting and production-ready code.

Responsibilities

• Design, develop, and refine time series forecasting models for weather, energy prices, and grid supply/demand.
• Translate forecasting algorithms into robust, production-ready code for a live energy trading platform.
• Analyze datasets, identify patterns, and improve forecasting accuracy.
• Incorporate agentic coding tools into daily development workflows to accelerate implementation.
• Collaborate with data pipelines and production environments to ensure reliability.

Skills

Python

SQL

Time series forecasting

Go (adv)

English communication

AI-assisted coding tools

Education

Bachelor's degree

Tools

Agentic coding tools

Python pipelines

Job description

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 Turkey.

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.