Role Description - Analyze and support market risk and credit risk models - Understand and validate market data inputs and data anomalies - Interpret and explain risk model outputs and calculations - Be responsible for regular model calibration processes, including back testing and analyzing results, and authorizing publication - Collaborate with quant managers, risk teams, and developers - Contribute to development and support of risk technology platforms Qualifications - Post Graduate degree in mathematics/Statistics/Physics with min 2yrs of relevant work experience and certification in risk management like FRM or PRM - Master’s degree in quantitative finance - MBA or PG Diploma in management with good understanding of financial markets and products Requirements - Strong quantitative and mathematical background - Experience in market risk / credit risk modeling or analytics - Hands-on experience with risk models and financial data - Working experience with SAS or similar analytics tools - Strong communication skills to explain quantitative results Benefits - We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized - We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace

• Build and validate ML models supporting pricing, promotion, and markdown optimization. • Contribute to GenAI initiatives — Build vertical-domain agents and agent clusters. • Partner with Data Engineering to build robust, production-grade data pipelines. • Perform exploratory data analysis and translate retail/CPG data into actionable insights. • Build dashboards and visualizations to communicate findings to product and business stakeholders. • Participate in code review, model validation, and documentation practices. • Develop scalable feature engineering workflows over large retail datasets.

• Own the technical roadmap for ML/AI models powering price optimization, demand forecasting, promotion effectiveness, and markdown recommendations. • Architect and lead development of GenAI-powered agents and copilots (e.g., pricing copilots, demand intelligence agents) in partnership with engineering and product. • Set technical standards for model development, validation, and MLOps across the data science organization. • Mentor, coach, and grow a distributed team of data scientists, including direct oversight of the China and Poland-based team. • Partner with product and engineering leadership to translate retail and trade-promotion business problems into Data Science solutions. • Make build-vs-buy calls on LLM/GenAI tooling and vendor platforms together with ENG. • Present model performance, technical roadmap, and AI strategy to executive leadership and, where relevant, customers and prospects. • Track emerging AI/ML techniques and assess their applicability to retail and CPG use cases. • Develop scalable feature engineering workflows over large retail datasets.

Role Description We're hiring a Data Scientist in Poland to help build the ML and GenAI capabilities behind our next-generation retail and analytics platform. You'll work closely with the Lead Data Scientist and a distributed team to build models, ship GenAI features, and turn retail/CPG data into decisions that matter to our customers. Key Responsibilities - Build and validate ML models supporting pricing, promotion, and markdown optimization. - Contribute to GenAI initiatives — Build vertical-domain agents and agent clusters. - Partner with Data Engineering to build robust, production-grade data pipelines. - Perform exploratory data analysis and translate retail/CPG data into actionable insights. - Build dashboards and visualizations to communicate findings to product and business stakeholders. - Participate in code review, model validation, and documentation practices. - Develop scalable feature engineering workflows over large retail datasets. Qualifications - 3+ years of experience in data science or applied ML roles. - Experience designing, building, and shipping models for price optimization, demand forecasting, promotion optimization, and similar retail/CPG use cases. - Strong analytical and problem-solving skills; comfortable working with imperfect, real-world retail data. Requirements - Proficiency in Python, SQL, and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). - Familiarity with GenAI frameworks (e.g., LLMs, Dify, LangChain, RAG pipelines). - Familiarity with cloud-based data platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Spark, Hadoop, Databricks). - Experience with data visualization tools (e.g., Power BI, Tableau) and modern MLOps practices. - Hands-on experience with modern data tooling (e.g., dbt, Airflow, or similar orchestrators) and columnar/analytical engines.