• 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.

Role Description We're hiring a Lead Data Scientist to own the technical roadmap for the ML and GenAI capabilities powering our next-generation retail and analytics platform — from price optimization and demand forecasting to promotion effectiveness and AI-driven copilots. You'll set the technical bar for the data science organization and lead a distributed team, including our Poland-based data scientists. - 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. Qualifications - 7+ years of experience in data science or applied ML, including 2+ years leading data scientists or a technical team. - Proven track record shipping production ML models at scale. - Experience designing, building, and shipping models for price elasticity, demand forecasting, promotion effectiveness, and similar retail/CPG use cases. - Strong communication skills — able to translate technical work into business impact for executives and customers. 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.

Role Description As a Senior Data Scientist, you will play a pivotal role in our data science efforts, with responsibilities including, but not limited to, the following: - Advanced Analytics: Apply state-of-the-art data science techniques to analyze and extract meaningful insights from vast and diverse datasets. - Predictive Modeling: Develop and implement advanced predictive models to forecast key risk factors relevant to insurance and potentially other use cases. - Collaboration: Collaborate closely with our data engineers, product managers, and other business stakeholders to translate data insights into actionable strategies and solutions. - Data Visualization: Create compelling data visualizations and reports to communicate findings effectively to both technical and non-technical audiences. - Research and Innovation: Stay at the forefront of data science research, exploring new methodologies and technologies to drive innovation within the company. Qualifications - 3-5 years proven track record as a Data Scientist working with large datasets (e.g., millions of rows), from prototyping to business impact, analytics and ML use cases. - Deep understanding of machine learning and statistical methods with their underlying theory and math. - Demonstrated experience in building, deploying, and showing business value from predictive models and data products. - Highly proficient in Python. - Proficiency in SQL databases, understanding schemas, and data types. - Real-world experience with at least one distributed data platform (preferably Spark). - Solid software development experience, including translating ML models into production software, especially in collaboration with other engineers. - MS or PhD in a quantitative discipline, especially Statistics, Math, or similar. - Strong communication skills. Requirements - Deep Learning, especially Transformers. - Experience with GLMs, including for actuarial frequency applications. - Boosting algorithms (CatBoost, XGBoost, etc.). - Experience with distributed machine-learning frameworks, like Spark, etc. - Creating data pipelines for analytics or ML applications. - Experience using AWS (EC2, EMR, S3, etc.) or similar cloud provider (Google, Azure, etc.). - Resourceful self-starter and team player with strong leadership skills. - Uncompromising attention to details. - Proven ability to be creative and resourceful in a fast-paced, entrepreneurial environment. - Must be comfortable working independently. - React well and quickly to frequent project demands and requirement changes. - Excellent analytical, troubleshooting, and problem-solving skills. - Strong written and communication skills and positive attitude working with customers and partners.