Manager Data Science (930982)

The Manager, Data Science will be a core member of the Enterprise Performance & Governance team, translating complex and often fragmented data into clear insights, practical tools, and action. The role will support enterprise performance priorities while also partnering with functional areas such as Finance, Procurement, Legal, Operations, and other corporate and business teams to organize, streamline, and advance analytical projects in these realms.

This is a hands-on role for a curious, self-directed problem solver. The Manager, Data Science will combine coding, statistical methods, business intelligence, data visualization, and responsible use of artificial intelligence to investigate questions, improve recurring analyses, automate manual work, and identify better ways to solve enterprise problems. Success requires the ability to move from an ambiguous question to a well-structured analysis with minimal direction, while communicating findings in a form that leaders and functional partners can readily use.

 Advanced Analytics & Problem SolvingFrame ambiguous business questions as structured analytical problems, identify the data and methods required, and independently advance work from exploration through recommendation.Conduct descriptive, diagnostic, predictive, and prescriptive analyses to identify performance drivers, emerging risks, opportunities, and potential interventions.Apply statistical methods, scenario analysis, forecasting, and other analytical techniques as appropriate to the business question.Perform root-cause analysis when financial, operational, quality, or workforce performance deviates from expectations, and translate findings into practical actions.Validate assumptions, test analytical outputs, document limitations, and communicate an appropriate level of confidence in conclusions.AI-Enabled Analysis & InnovationUse AI tools to accelerate research, coding, data preparation, analysis, documentation, and problem solving.Identify repeatable opportunities to embed AI, automation, or advanced analytics into enterprise and functional workflows.Prototype new analytical approaches and tools, evaluate their usefulness, and convert successful concepts into scalable and maintainable solutions.Stay current on relevant developments in data science, artificial intelligence, analytics engineering, and visualization, and translate those developments into practical business applications.Business Intelligence & Data VisualizationDesign, build, and maintain intuitive Power BI dashboards, scorecards, and analytical products that provide timely visibility into enterprise, business-unit, contract, and functional performance.Develop clear data visualizations that surface trends, exceptions, relationships, and risks without overwhelming the intended audience.Partner with stakeholders to define metrics, business rules, thresholds, and reporting logic, helping create consistent definitions across the organization.Create executive-ready analyses and visual narratives that connect technical findings to business impact, decisions, and action.Data Preparation, Coding & AutomationUse coding and query languages, such as Python, SQL, M, DAX, and Power Query, as applicable, to acquire, clean, transform, join, analyze, and model data from multiple sources.Build reusable analytical workflows and automate recurring data preparation, quality checks, reporting, and analysis to reduce manual effort and increase reliability.Assess data quality, completeness, and usability; proactively identify gaps and work with partners to improve the underlying data and process.Enterprise & Cross-Functional PartnershipPartner with APM Operations Leadership to strengthen performance visibility, early-warning capabilities, root-cause analysis, and corrective-action insight.Provide analytical support to Finance, Procurement, Legal, Operations, and other functions, adapting methods and deliverables to each team’s business context.Work across organizational boundaries to obtain information, reconcile differing definitions, and develop solutions that balance functional needs with enterprise consistency.Build trust with technical and non-technical stakeholders by asking strong questions, explaining analytical choices, and delivering useful work with limited oversight.Contribute to the development of an enterprise analytics community, including reusable standards, methods, templates, and examples.