Markdown and Pricing Optimization Data Scientist
On-site · Poznań, Greater Poland, Poland
Job Summary
Data Scientist will build machine learning models to optimize markdown, forecast demand, predict sell-through, and manage inventory liquidation; develop pricing and promotion recommendation algorithms from historical sales and commercial data; estimate price elasticity and promotional impact; design and maintain datasets, data pipelines, and data lake quality; analyze and monitor model performance; contribute to deployment of markdown optimization tools; collaborate with Planning, Pricing, Buying, Commercial, and Technology teams, and translate analytical findings for non-technical stakeholders; support automation and digitalization of pricing and promotional decision-making processes.
Required Qualifications
- Master’s degree in a quantitative field (e.g. Data Science, Statistics, Mathematics, Econometrics, Computer Science, Operations Research, Engineering or Economics)
- 2–4 years of experience in Data Science, Machine Learning, Advanced Analytics, or Quantitative Modeling (experience in retail, e-commerce, pricing, revenue management, merchandising, supply chain, or commercial analytics is preferred)
- Experience developing predictive models using large datasets
- Experience working with SQL and Python in a production or business environment
- Advanced Python programming skills and strong SQL knowledge
- Strong knowledge of statistics, predictive modeling, and machine learning techniques
- Experience with machine learning libraries (Scikit-learn, XGBoost, TensorFlow, PyTorch or equivalent)
- Experience working with large and complex datasets and understanding of data modeling, data pipelines, and data lake concepts
- Experience with cloud platforms (preferably Azure) and data visualization
- Knowledge of MLOps principles and model deployment practices
- Knowledge of optimization techniques, forecasting, and recommendation systems
- Strong analytical, problem-solving, presentation and comunication skills
- Ability to explain complex analytical concepts to non-technical audiences
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