Senior Manager Data Science

Purpose & Overall Relevance for the Organization: As part of the Emerging Markets Data & Analytics (DNA) team, the Senior Manager Data Science & AI will lead the Data Science & AI pillar , driving E2E innovation, experimentation, and ideation to delivery of advanced analytics solutions across EM functions and clusters. This role is pivotal in enabling data-driven decision-making and unlocking new opportunities and business growth through advanced analytics capabilities, including machine learning, generative AI, and agentic systems. The role will own the end-to-end lifecycle of advanced analytics solutions , from scoping and development to deployment and operationalization, while ensuring alignment with business priorities and global standards. It will also foster collaboration with Global Data & AI , Global Digital Analytics , and e-com EM Data Science , ensuring cross-functional enablement and technical excellence. Key Responsibilities: Advanced Analytics & Data Science Drive the design, development, and deployment of advanced analytics models (e.g., forecasting, classification, clustering, recommendation systems). Hands-on build and development is expected from Day 1, with direct ownership of advanced analytics pillar. Translate business problems into data science use cases and actionable solutions, closely collaborating with the BI Analytics DNA pillar. Drive or support experimentation and implementation of GenAI , Agentic AI , and LLM-based solutions tailored to EM business needs. Ensure model performance, interpretability, and business relevance. ML Ops & Technical Enablement Co-ownership the ML Ops lifecycle together with the Data Engineering pillar: model versioning, CI/CD pipelines, monitoring, retraining, and governance. Collaborate with Data Engineering to ensure scalable data pipelines and integration with Lakehouse (Databricks). Maintain and evolve the EM DS-Fusion quantum for advanced analytics experimentation. Strategic Collaboration & Cross-Functional Enablement Partner with EM business functions (e.g., Finance, Brand, Commercial, e-com, SCM, Merchandising…) to scope and prioritize high-impact use cases. Collaborate with Global Data & AI on modular solution rollouts, upgrades, and experimentation. Engage with Global Digital Analytics and e-com EM Data Science to ensure alignment, avoid duplication, and promote best practices. Support key cross-functional topics (e.g. Read & React, Trading tools) with ownership of modelling, upgrades and expansions. Innovation & Thought Leadership Stay abreast of emerging trends in AI, ML, GenAI, and agentic systems. Promote a culture of innovation, agile piloting, and continuous learning within the DNA team. Represent EM DNA in global forums and contribute to the broader adidas analytics and Data Science community. Key Relationships: EM DNA Pillars: Data Engineering, BI Analytics Global Data & AI (Central DNA) Global Digital Analytics (Sales & Trading) EM Business Functions (Finance, Brand, Commercial, e-com, SCM, Merchandising) Requisite Education and Experience / Minimum Qualifications: Education & Professional Experience University degree in Data Science, Computer Science, Mathematics, Engineering, or related field. 7+ years of experience in data science, machine learning, and AI. Proven track record of delivering advanced analytics solutions in a business context. Experience in driving data science projects end-to-end. Technical Skills Strong proficiency in Python, SQL, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Experience with ML Ops tools (e.g., MLflow, Airflow, Databricks). Familiarity with LLMs , GenAI frameworks , and agentic architectures . Experience with cloud platforms (Azure, AWS, GCP) and data lakehouse architectures. Solid understanding of data governance, model monitoring, and compliance. Soft Skills Strategic thinker with strong business acumen. Excellent communication and stakeholder management skills. Ability to translate complex technical concepts into business value. Passion for innovation and continuous improvement. What Success Looks Like Deployment of scalable, high-impact data science solutions across EM. Seamless collaboration with global teams and local functions. Acceleration of GenAI and agentic AI experimentation. Establishment of a robust ML Ops framework within EM DNA. Recognition of EM DNA as a strategic enabler of growth and innovation.