Data Scientist - Logistics AI & ML

Salary

PLN 14,600–20,825, depending on skills and under a contract of employment with tax-deductible costs.

Annual bonus based on individual annual performance and company results.

Description

Build data-driven products for logistics-process optimization, volume management, and delivery-time prediction. Develop core AI systems that dynamically optimize delivery costs and improve network efficiency, including parcel-locker and courier operations. Work with product, engineering, and operations teams to deliver AI/ML systems running in production.

Responsibilities

• Create complex data models using machine learning, deep learning, time-series forecasting, and survival models.
• Use mathematical optimization techniques, including MILP and OR-Tools, to solve logistics problems.
• Design, develop, maintain, and research core production AI/ML systems and methodologies.
• Use Python and advanced SQL to process, automate, and analyze large datasets while ensuring high-quality input data.
• Work with engineering teams to improve the availability, integrity, accuracy, and scalability of ML pipelines and workflow orchestration.
• Translate business challenges and supply-chain bottlenecks into AI/ML or optimization problems; test novel approaches to minimize costs.
• Collaborate with stakeholders throughout the AI/ML lifecycle, communicate advanced insights clearly, and share knowledge within the team.

Requirements

• At least 2 years of experience building Machine Learning and Artificial Intelligence solutions deployed in production.
• Deep understanding of AI/ML methods, particularly decision tree-based algorithms, time-series models, and neural networks.
• Proficiency in Python and comfort with software-development tools.
• Experience with Pandas/Polars, Scikit-learn, CatBoost, Optuna, Prophet, PyTorch, and LangGraph.
• Advanced SQL knowledge.
• Ability to translate business challenges into AI/ML problems and solve them with novel approaches.
• Polish at B2 level and English at B2 level.
• Degree in a quantitative or statistical/mathematical modeling discipline, such as Data Science, Mathematics, Physics, Econometrics, Computer Science, Engineering, Logistics, or a similar major.

Nice to Have

• Workflow orchestration and AI-platform experience, including Airflow, Google Cloud Composer, Gemini Enterprise Agent Platform, and Vertex AI.
• Familiarity with Operations Research, mathematical optimization techniques such as MILP, and graph analytics.
• Experience with geospatial data and libraries such as GeoPandas and H3.

What’s in It for You

• Flexible hybrid work: 4 days in the office and 1 day remote; start between 7:00 a.m. and 10:00 a.m.; 30 days of occasional remote work.
• Annual performance and company-results bonus.
• Well-equipped offices, including kitchens, bicycle parking, green terraces, raised desks, ergonomic chairs, and interactive conference rooms.
• Choice of a 16-inch or 14-inch MacBook Pro, or a corresponding Dell Windows laptop, with required accessories.
• Cafeteria-plan benefits, including medical, sports, lunch, insurance, and purchase-voucher options.
• Job-related English classes, training budget, hackathons, internal learning platform, and an additional volunteering day off.
• Social events and team activities.

Working Environment

• Work with a team of specialists with a strong knowledge-sharing culture, autonomy in team organization, and freedom to choose suitable technologies.
• Access to a self-service engineering ecosystem and tools including Kubernetes, Docker, Consul, GitHub, and GitHub Actions, plus modern AI tools for automating repetitive work.
• Build solutions used at large scale across a microservices environment, an open-source Hermes data bus, service mesh, petabyte-scale data, and production machine learning.