Markdown and Pricing Optimization Data Scientist

Oferujemy

• Work in an international company featured on the Forbes list of the best employers in Poland and beloved by millions of consumers in Poland and Europe.

• A competitive salary and real opportunities for promotion - the development of our employees is the cornerstone of our strategy!

• Life insurance on preferential terms, to feel secure.

• An extensive benefits platform offering a wide range of options – including medical care, sports cards, events, trips, shopping vouchers, and more.

• Discounts on purchases at Pepco stores – get your favorite products at even better prices!

• A wide range of training sessions, courses, and webinars designed to help our employees grow and thrive!

• The opportunity to improve language skills by working in an international environment.

• Free psychological, legal, and financial consultations as part of our employee support program – our employees' well-being is our top priority!

• Smart Lunch – meal subsidies for the company canteen, giving access to daily on-site dishes at discounted prices.

• Financial recognition in the form of jubilee awards, additional days off, and loyalty-based discounts for employees celebrating their work anniversaries.

• A social fund providing additional financial support to ensure security and assistance in various life situations.

• A friendly atmosphere and support from colleagues who are eager to share their expert knowledge – despite being a large organization, we’ve maintained a warm, informal atmosphere.

• A role within a unique organizational culture – Pepcoolture – where our mission, vision, and values are the foundation of our everyday work.

Wymagania

• 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.

• Master’s degree in a quantitative field (e.g. Data Science, Statistics, Mathematics, Econometrics, Computer Science, Operations Research, Engineering or Economics).

• English proficiency at minimum B2 level (C1 preferred).

• 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 comumunication skills.

• Ability to explain complex analytical concepts to non-technical audiences

Zakres obowiązków

• Build machine learning models supporting markdown optimization, demand forecasting, sell-through prediction, and inventory liquidation.

• Develop pricing and promotion recommendation algorithms based on historical sales, inventory, customer behavior, and commercial data.

• Estimate and monitor key commercial drivers such as price elasticity, promotional effectiveness, and markdown impact.

• Design, develop, and maintain analytical datasets supporting markdown and pricing optimization initiatives.

• Contribute to the development and enhancement of the company's data lake, data pipelines, and data quality standards.

• Conduct advanced statistical analyses.

• Translate analytical findings into business recommendations supporting Pricing, Planning, and Commercial teams.

• Develop simulation and scenario-planning capabilities to support strategic decision making.

• Support the design, development, testing, and deployment of markdown optimization tools and recommendation engines.

• Monitor model performance and continuously improve prediction accuracy and business impact.

• Participate in automation and digitalization initiatives related to pricing and promotional decision-making processes.

• Collaborate with Planning, Pricing, Buying, Commercial, and Technology teams to identify optimization opportunities.

• Communicate complex analytical concepts and model outputs to non-technical stakeholders.

• Support change management and adoption of data-driven decision-making practices.