Decision Intelligence Lead

The Decision Intelligence Lead, within the Operations Intelligence & Analytics organization, drives the product strategy, roadmap, and delivery of executive analytics solutions that translate Global Operations initiatives into measurable business outcomes. This role transforms complex operational data into actionable insights delivered through dashboards, scorecards, and interactive applications that improve decision-making, performance, and efficiency across the enterprise.

As a key partner to leaders across Supply Chain, Manufacturing, Quality, Regulatory Affairs, and other Global Operations functions, the Decision Intelligence Lead operates with a product-oriented approach to analytics, overseeing solutions from strategy and requirements through deployment and continuous enhancement. The ideal candidate combines expertise in business intelligence, modern data platforms, and advanced analytics with the ability to translate business needs into scalable, user-centric solutions that influence executive strategy and operational performance.
 
Responsibilities:

• Build and own the product roadmap, including the executive insights dashboard, data-rich executive summaries, and balanced scorecard reporting.
• Apply product strategy, management and UX principles to design scalable and intuitive data products with the user in mind.
• Develop decision intelligence products within Power BI and Microsoft Fabric, including semantic models, KPI frameworks, dashboards, and executive summaries that provide actionable insights into the health and performance of the global operations network.
• Build interactive scenario planning applications, incorporating predictive models to support trade-off analysis and forward-looking decision-making.
• Automate repeatable data extraction, preparation and transformation workflows using Python and SQL.
• Integrate ERP data from SAP, Oracle and additional disparate data sources into cohesive, consumable decision intelligence products.
• Apply predictive analytics and AI/ML techniques, where appropriate, to enhance operational forecasting, risk identification, scenario simulation, and decision support capabilities.
• Use standard DevOps tools, including GitHub, Confluence, and Jira, throughout the product development lifecycle.

• Bachelor's degree in Data Engineering, Data Science, Computer Science, Statistics, or a related field, or equivalent professional experience; Master's degree preferred.
• 8+ years of experience in data science, business intelligence, or analytics, with deep expertise in modern BI architecture and tools.
• Expertise in Power BI, including proficiency in DAX for building measures, KPIs, and time-intelligence calculations; broad experience in other tools such as Spotfire and Qlik.
• Highly proficient in Python (e.g., pandas, NumPy) and SQL, with hands-on experience data wrangling across disparate sources.
• Hands-on experience developing front-end/interactive applications using low-code and full-code frameworks such as Power Apps, Plotly Dash, or React.js.
• Experience leveraging DevOps and collaboration platforms, including GitHub, Confluence, and Jira, to support agile delivery, version control, documentation, and product lifecycle management.
• Strong executive communication, data storytelling, product strategy, and UX design skills, with a demonstrated ability to transform complex data into intuitive user-centric analytics products.
• Previous experience with unified analytics and lakehouse platforms such as Microsoft Fabric or Databricks, and familiarity with broader cloud data ecosystems (AWS, Snowflake).
• Experience working within large matrixed organizations, understanding business workflows and leveraging data from enterprise ERP platforms (e.g., SAP, Oracle).
  

Preferred Qualifications:

• Previous experience supporting global supply chain, manufacturing, quality, or regulatory operations in a complex enterprise environment.
• Familiarity with native GenBI capabilities (e.g., Copilot in Power BI/Fabric) and GenAI application development process.
• Practical experience across the ML lifecycle, including feature engineering, model selection, training and validation, deployment, and performance monitoring/retraining, using frameworks such as scikit-learn, XGBoost, or equivalent.
• Relevant Microsoft Fabric, cloud or data engineering certifications such as MS DP-600 or MS DP-700.