Requirements

Expected technologies

Python

Optional technologies

Microsoft Azure

Databricks

Our requirements

• You are proficient in Python and have some experience with software development. You can write clean, well-structured, readable, efficient code.
• Experience with agent-based optimization frameworks and policy development, reinforcement learning, game-theoretic and adversarial approaches, self-supervised training, curriculum learning, RAGs and knowledge graph design are a plus.
• At least 3 years of proven hands-on experience in data science, statistical modeling, or analytics, with a good understanding of business processes.
• You are a strong problem-solver; you can translate operational and organizational problems into mathematical abstractions, propose new strategies, develop action plans, and deliver products and actionable recommendations in a timely manner.
• You are a good communicator and can effectively cooperate with peers and clients. You are clear, curious, kind, enthusiastic, and a good presenter. Fluent business English is necessary.
• You are curious and open to exploring existing supply chain tools, to understand their strengths and weaknesses.

Optional

• Experience with Azure or other cloud platforms, Databricks, DevOps, Data Engineering, Agile principle.
• Experience in the Supply Chain areas (Planning, Demand Forecasting, Optimization).

Your responsibilities

• Collaborate with our data science team, business and technical stakeholders to define, design, and deliver analytical solutions aligned with supply chain objectives.
• Analyze business processes (e.g., demand allocation, supply planning, inventory optimization), translate them into mathematical formulations, sketch proof of concepts, test and validate solutions, and work with solution architects to deploy to production-grade solutions and platforms.
• Design, implement, and maintain robust, scalable Python libraries and data pipelines, supporting production-grade workflows.
• Collaborate with other developers to build modular, reusable components to streamline experimentation, deployment, and performance monitoring.
• Champion software quality best practices through unit testing, code reviews, documentation, and continuous integration to ensure long-term maintainability and reliability.
• Analyze datasets across multiple systems to identify business opportunities and develop data-driven strategies.
• Apply statistical and machine learning techniques and stay up to date with emerging technologies in data science and supply chain
• Document model assumptions, constraints, and results in a clear, business-oriented way.
• Transform analytical outputs into insights and actionable recommendations for clients.
• Present analytical results visually and verbally to technical and non-technical audiences.