Data Science Specialist

Data Science Specialist

Driven by our expanding analytics roadmap, Aygaz is growing its Data Analytics Team and seeking a Data Scientist who will help scale data-driven decision making across the company. In this role, you will work on high-impact business problems by transforming data into actionable insights, supporting the development of analytical and machine learning models, and collaborating closely with business and technology teams. You will contribute to real-world projects such as logistics operations planning and optimization, pricing and customer analytics, while developing your skills in a dynamic environment with strong leadership support and a clear analytics vision.

Qualifications

• BSc or MSc degree in Data Science, Computer Engineering, Mathematics Engineering and Industrial Engineering
• 1+ years of experience building data science/statistical models (Multivariate regression, time series models, XGBoost, causal inference, deep learning frameworks such as PyTorch, etc.)
• Proficiency in Python and SQL for data manipulation, analysis, and model development.
• Strong communication skills
• Fluency in English
• Proactive, dynamic, open-minded, team-work and result oriented

Job Description

• Develop end‑to‑end analytics and machine learning solutions for business problems.
• Work on real‑world analytics projects in route optimization, operations planning, pricing and customer analytics.
• Perform exploratory and advanced data analysis on large datasets.
• Build and improve time series , forecasting and optimization models to support decision processes.
• Model and solve optimization problems such as supply chain networks, vehicle routing and delivery area planning
• Implement polygon processing and map services , including GIS-based applications
• Build and deploy agentic AI systems and LLMs
• Design and maintain BI dashboards and reports to track KPIs and performance metrics.
• Translate analytical results into clear insights for technical and non‑technical stakeholders.
• Collaborate with business and technology teams during model development and deployment.
• Monitor model performance and ensure continuous improvement of analytics solutions.