Data Engineer
Data Science Specialist
Istanbul
Apply on the employer's siteRole description
Data Science Specialist
Aygaz Data Analytics Team plays a central role in the company’s data-driven transformation agenda, delivering strategic, executive-sponsored initiatives that improve operational efficiency, strengthen sales and profitability, and enable scalable decision-making across core and emerging business areas. Our portfolio brings together optimization, forecasting and pricing analytics, geospatial planning, deep learning, selected image-based analytics, and LLM-enabled automation to solve complex business problems with measurable impact. We work closely with business units to understand Aygaz’s end-to-end domain, translate problems into analytical solutions, and build standardized, insight-led processes that can be adopted across the organization. In this role, you will own assigned analytical workstreams within this portfolio while learning from experienced data scientists, data architects and business stakeholders.
Qualifications
- BSc or MSc degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, Industrial Engineering, or a related quantitative field
- 1–3 years of hands-on experience in data science, analytics, machine learning, or optimization projects
- Strong practical skills in Python and SQL for data preparation, analysis, modeling and experimentation
- Working knowledge of modeling concepts such as forecasting, regression, classification, tree-based models, or deep learning
- Curiosity to explore tools such as PyTorch, NVIDIA stack, Gurobi, GeoPandas/GIS, and LLM frameworks with expert guidance
- Ability to understand business problems, question assumptions, communicate insights clearly, and work collaboratively with technical and non-technical stakeholders
- Good command of English
Job Description
- Own assigned analytical workstreams from problem framing and data exploration to production-ready solutions in selected areas of our portfolio.
- Route and fleet optimization for smarter distribution planning, field operations, and logistics efficiency.
- End-to-end LPG supply and inventory planning, from sourcing and vessel operations to inter-facility transfers and sales dispatches.
- Demand forecasting, pricing analytics, customer analytics, and decision-support models using structured and unstructured data.
- Deep learning models and selected image-based analytics use cases that support automation and performance improvement.
- GIS-based analytics, polygon processing, and map-enabled planning for network and territory design.
- LLM-driven insights, intelligent assistants, chatbot capabilities, and agentic business workflows.
- Turn analytical outputs into clear recommendations for business stakeholders.
- Collaborate with data architecture, BI, and technology teams to deploy, monitor, and continuously improve solutions.
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