Job's mission

As a Senior Specialist I, Business Analytics, you will turn complex supply-chain data into decisions that help ASM keep critical parts available for our customers while improving inventory, cost, and lead-time performance. You will own and develop forecasting, inventory, capacity, and optimization models, working across Spares Supply Chain, Operations, Procurement, Logistics, Service, Finance, and IT to solve real operational challenges. Your insights will go beyond reporting, translating data, business context, and scenario analysis into practical recommendations that strengthen customer service and supply-chain resilience.

What you will be working on

• Develop demand forecasts using historical demand, installed-base data, customer commitments, service requirements, equipment plans, consumption patterns, and other relevant business drivers.
• Apply statistical and machine-learning methods to intermittent, lumpy, seasonal, and rapidly changing demand across parts, product families, customers, regions, warehouses, and suppliers.
• Build expected, upside, downside, and constrained-demand scenarios, and evaluate forecast performance using measures such as WAPE, MAE, forecast bias, Forecast Value Added, and service-level outcomes.
• Translate forecasts into practical recommendations for purchasing, inventory, supplier capacity, and allocation decisions.
• Design inventory policies covering safety stock, reorder points, replenishment parameters, target stock levels, and order quantities to balance availability, working capital, obsolescence risk, and logistics cost.
• Segment inventory using dimensions such as ABC value, XYZ demand variability, criticality, lead time, lifecycle, and supply risk, identifying shortage risks as well as excess, obsolete, slow-moving, and single-source inventory.
• Analyze demand, backlog, material availability, and supplier and operational capacity to identify bottlenecks and anticipate risks to customer commitments.
• Develop allocation, load-balancing, inventory-rebalancing, and optimization models across suppliers, sites, warehouses, transportation routes, and operational resources.
• Run what-if scenarios for demand surges, supplier disruptions, shortages, capacity constraints, alternate sources, and expedited shipments, quantifying the service and financial impact of different decisions.
• Analyze end-to-end lead time and logistics cost, identifying opportunities in transportation, consolidation, inventory positioning, supplier performance, customs, receiving, inspection, and internal handling.
• Combine quantitative evidence with insights from planners, suppliers, customers, procurement, service teams, and subject-matter experts, clearly documenting assumptions and professional judgment.
• Partner with IT and data engineering teams to define analytical datasets, ETL/ELT requirements, semantic models, common data definitions, validation controls, and production-ready analytical solutions.
• Strengthen data quality by addressing missing values, duplicates, unit-of-measure inconsistencies, master-data mismatches, incorrect lead times, and demand-history anomalies.
• Collaborate on dashboards, analytical tools, and early-warning solutions that help teams act on shortages, forecast changes, supplier-capacity risks, excess inventory, lead-time deterioration, premium freight, and customer-commitment risks.
• Track the results of implemented recommendations and communicate complex analytical findings clearly to technical and non-technical stakeholders.

What we are looking for

• Bachelor’s degree in Data Science, Business Analytics, Operations Research, Industrial Engineering, Supply Chain Management, Statistics, Computer Science, or a related discipline.
• Around five or more years of relevant experience in supply-chain analytics, demand forecasting, inventory optimization, operations research, logistics analytics, or a comparable field.
• Strong knowledge of demand forecasting, inventory planning, safety stock, reorder policies, supply and capacity planning, lead-time analysis, and logistics-cost analysis.
• Strong SQL skills and practical experience using Python or R for data analysis and analytical model development.
• Experience using Power BI or an equivalent data-visualization and business-intelligence platform.
• Experience working with ERP and supply-chain datasets, preferably SAP.
• Working knowledge of ETL/ELT processes, relational data models, data-quality practices, and analytical pipelines.
• Ability to translate analytical results into clear business recommendations and explain model assumptions, limitations, risks, and trade-offs.
• Strong problem-solving and stakeholder-management skills, with the ability to collaborate effectively across technical and business teams.

What sets you apart

• Master’s degree in Operations Research, Supply Chain Analytics, Data Science, Industrial Engineering, Statistics, or a related discipline.
• Experience in semiconductor equipment, spare parts, aftermarket service, advanced manufacturing, or another high-mix, variable-demand environment.
• Experience applying intermittent-demand techniques such as Croston, SBA, TSB, or probabilistic forecasting.
• Practical experience with optimization techniques such as linear programming, mixed-integer programming, network optimization, simulation, or constraint programming.
• Experience using analytical libraries and tools such as pandas, scikit-learn, statsmodels, XGBoost, OR-Tools, PuLP, or Pyomo.
• Familiarity with enterprise data platforms such as Databricks, Azure Data Factory, Azure Synapse, or equivalent platforms.
• Relevant professional credentials such as ASCM CPIM, ASCM CSCP, Microsoft Power BI Data Analyst, or Databricks certification.