Associate Director, Forecasting Analytics

Role Overview

The AI & Analytics Leader – Forecasting will own the end-to-end design, development, and scaling of advanced forecasting solutions leveraging AI/ML. This role will drive predictive intelligence across SIOP, demand planning, inventory optimization, and aftermarket growth, translating business problems into scalable AI products.

• AI Strategy & Vision – Define the AI roadmap for forecasting across regions and business units; identify high-value use cases (demand sensing, pricing elasticity, parts forecasting, supply optimization); align AI initiatives with enterprise value levers (revenue growth, cost optimization, service levels).
• Model Development & Deployment – Design and deploy advanced models (time‑series, probabilistic forecasting, causal ML, GenAI copilots); lead experimentation frameworks (A/B testing, backtesting, scenario simulations); ensure scalable deployment via MLOps pipelines.
• Productization of AI Solutions – Convert models into reusable AI products (forecasting engines, APIs, copilots); work closely with product managers to define features, roadmaps, and adoption strategies; drive standardization across markets while enabling localization.
• Business Impact & Value Realization – Quantify and track value (forecast accuracy improvement, inventory reduction, revenue uplift); partner with Finance and business teams for value baselining and realization; continuously optimize models based on performance and feedback loops.
• Stakeholder & Team Leadership – Collaborate with SIOP leaders, supply chain teams, and digital leadership; lead a team of data scientists, ML engineers, and analysts; act as a thought leader in AI within the organization.

• 10–15 years experience in AI/ML, with strong focus on forecasting or supply chain analytics.
• Deep expertise in time‑series forecasting, ML, and optimization techniques.
• Experience with MLOps, cloud platforms (GCP/AWS/Azure), and data ecosystems.
• Strong business acumen in supply chain/SIOP processes.
• Proven ability to drive AI adoption at scale.

• Forecast accuracy improvement (%).
• Inventory reduction / working capital impact.
• Revenue uplift from AI‑driven insights.
• Adoption rate of AI products across BUs.

CHF  – CHF