DemandTec is a retail analytics and demand science platform used by grocery retailers and CPG suppliers to run pricing, promotions, markdowns, and trade fund decisions on connected, AI-powered intelligence rather than siloed point solutions. Backed by Longshore Capital, we're modernizing a market-leading core into an AI/ML-native, composable platform — with a live network of 7,800+ connected CPG suppliers, the largest and hardest-to-replicate asset in the category. We're building the next generation of our solution platform for retail and analytics: GenAI-powered agents, real-time decisioning, and connected optimization across pricing, promotions, markdowns, and trade funds. This team sits at the center of that build. 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. **Required 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. **Technical Skills** • **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.