Data Scientist - Retail Analytics & AI (all genders)

Shape the Future of Data-Driven Retail Decisions At GK, a global leader in retail technology and part of the Fujitsu Group, you will help retailers transform data into better business decisions. By combining advanced analytics, machine learning, and mathematical optimization, you will work on innovative customer projects that improve pricing, forecasting, inventory management, and operational efficiency. Location: Germany - preferably in the Saxony region. Employment: Full-time, Hybrid. Contract: Fixed-term until July 2028 (parental leave replacement). Your Mission As a Data Scientist, you will work closely with our customers and consulting teams to solve complex retail challenges through data-driven approaches. You combine strong analytical thinking with curiosity for new methods and a creative, solution-oriented mindset. Your business acumen enables you to turn data insights into real customer value, and you communicate complex findings clearly to any audience. What you'll do Solve highly relevant retail challenges, including Demand Forecasting, Pricing Optimization, Markdown Optimization, Inventory Analytics, Assortment Optimization and AI-Assisted Decision Support. Analyze large and complex datasets from retail and e-commerce environments. Clean, validate, and prepare structured and unstructured data to ensure high data quality. Identify relevant patterns, relationships, and business drivers within customer data. Develop and apply statistical, machine learning, and optimization models to solve business problems. Build demand forecasting and predictive analytics solutions. Evaluate model performance and continuously improve analytical approaches. Create meaningful visualizations and communicate complex analytical findings to business stakeholders. Collaborate with customers to understand business requirements and translate them into analytical solutions. Support the implementation of data-driven products and services within international customer projects. What You Bring University degree in Data Science and Artificial Intelligence, Mathematics, Statistics, Computer Science, Operations Research, Engineering , or a related quantitative discipline. Practical experience in data analytics, machine learning, optimization, or forecasting projects. Experience handling real-world datasets, ability to work with various types of data including data cleansing, preparation, and feature engineering. Ideally experience working in customer-facing projects or consulting environments. Strong Python and SQL skills , with experience using relevant data science libraries such as pandas, NumPy, scikit-learn, SciPy, Matplotlib, or Seaborn. Experience with machine learning techniques such as Regression methods, Classification models, Decision Trees, Ensemble Methods or Neural Networks. Knowledge of mathematical optimization methods is highly desirable, including Linear Programming, Mixed Integer Programming, Nonlinear Optimization, Heuristic approaches. Knowledge of data structures and experience working with large datasets to support data-driven decision-making. Excellent communication skills and the ability to explain complex analytical concepts to both technical and non-technical audiences. Excellent English; intermediate or higher proficiency in German. Why join GK? Mission-driven: We simplify interactions in retail – for businesses and customers alike. Empowered by flexibility: Remote-first, flexible hours – work where and how you thrive. Culture that connects: Open, collaborative, and people-first – with great coffee if you drop by 😉 Backed by strength: With 35+ years of retail tech expertise and as part of Fujitsu, a global IT leader. Tech that scales: AI-powered solutions built to grow with the world’s leading retailers. Think global: 570,000+ installations in 65+ countries. Real impact, worldwide. Ready to apply? If you love retail, live for solving problems, and want to do it alongside a team that genuinely has each other’s backs, we’d love to hear from you. Apply today and let’s build what’s next — together.