Manager Data Science (930982)

Equus•Louisville, KY

About The Position

The Manager, Data Science is a core member of APM’s Enterprise Performance & Governance team. The role turns complex and fragmented data into clear insights, practical tools, and action, supporting enterprise performance priorities and partnering with Finance, Procurement, Legal, Operations, and other corporate and business teams. This hands-on position combines coding, statistical methods, business intelligence, data visualization, and responsible AI use to investigate questions, improve recurring analyses, automate manual work, and address enterprise problems. The manager is expected to progress from ambiguous questions to structured analysis with minimal direction and communicate findings in a useful form for leaders and functional partners. The role conducts descriptive, diagnostic, predictive, and prescriptive analyses; applies statistical methods, scenario analysis, and forecasting; investigates performance variances; and validates assumptions, outputs, limitations, and confidence levels. It uses AI tools to support research, coding, preparation, analysis, documentation, and problem solving, while identifying opportunities for AI, automation, or advanced analytics in business workflows and developing successful prototypes into maintainable solutions. The manager designs and maintains Power BI dashboards, scorecards, and analytical products; develops visualizations and executive-ready narratives; and works with stakeholders to define metrics, business rules, thresholds, and reporting logic. The role uses languages and tools such as Python, SQL, M, DAX, and Power Query to prepare, transform, join, analyze, and model data, and builds reusable workflows and automation for recurring data preparation, quality checks, reporting, and analysis. The position partners with APM Operations Leadership and other functions, reconciles differing definitions, assesses data quality, and develops solutions that balance functional needs with enterprise consistency. It also contributes reusable standards, methods, templates, and examples to an enterprise analytics community. Travel is estimated at 10%.

Requirements

Bachelor’s degree in Mathematics, Statistics, Data Science, Computer Science, Economics, Engineering, or a related quantitative discipline.
At least 3 years of relevant professional experience in data science, advanced analytics, business intelligence, analytics engineering, or a closely related field.
Strong proficiency in at least one general-purpose analytical language such as Python or R, and working proficiency in SQL.
Hands-on experience developing Microsoft Power BI dashboards and analytical solutions, including data modeling, visualization design, and Power Query or comparable transformation tools.
Experience applying statistical and analytical methods to complex, real-world business problems and translating results into clear recommendations.
Experience working with multiple data sources and performing data cleaning, transformation, validation, and reconciliation.
Strong data visualization and written and verbal communication skills, including the ability to make complex findings understandable to non-technical audiences.
Ability to work independently, manage ambiguity, prioritize competing requests, and advance work with minimal direction.
Experience solving complex problems in a growing, changing, or operationally complex organization.

Nice To Haves

Master’s degree or other advanced degree in Data Science, Statistics, Mathematics, Computer Science, Operations Research, Business Analytics, or a related quantitative field.
Relevant certifications in advanced analytics, data science, artificial intelligence, cloud data platforms, coding, or Microsoft Power BI.
Experience using AI-assisted development tools in analytical workflows.
Experience with forecasting, predictive analytics, or other advanced analytical methods.
Experience with Microsoft Azure, Fabric, Databricks, Snowflake, or comparable cloud data and analytics platforms.
Experience supporting multiple corporate functions or working in a multi-business, contract-heavy, or service-delivery environment.
Familiarity with analytics governance, metric standardization, data quality controls, model documentation, and responsible AI practices.

Responsibilities

Frame ambiguous business questions as structured analytical problems and independently advance work from exploration through recommendation.
Conduct descriptive, diagnostic, predictive, and prescriptive analyses to identify performance drivers, risks, opportunities, and potential interventions.
Apply statistical methods, scenario analysis, forecasting, and other techniques appropriate to business questions.
Perform root-cause analysis of financial, operational, quality, or workforce performance deviations and translate findings into practical actions.
Validate assumptions and analytical outputs, document limitations, and communicate appropriate confidence levels.
Use AI tools to accelerate research, coding, data preparation, analysis, documentation, and problem solving.
Identify opportunities to incorporate AI, automation, or advanced analytics into enterprise and functional workflows.
Prototype analytical approaches and tools, evaluate their usefulness, and develop successful concepts into scalable, maintainable solutions.
Stay current with relevant data science, AI, analytics engineering, and visualization developments and apply them to business needs.
Design, build, and maintain Power BI dashboards, scorecards, and analytical products for enterprise, business-unit, contract, and functional performance.
Develop clear visualizations and executive-ready analyses that connect findings to business impact, decisions, and action.
Partner with stakeholders to define metrics, business rules, thresholds, and reporting logic and promote consistent organizational definitions.
Use coding and query languages such as Python, SQL, M, DAX, and Power Query to acquire, clean, transform, join, analyze, and model data.
Build reusable analytical workflows and automate recurring data preparation, quality checks, reporting, and analysis.
Assess data quality, completeness, and usability, identify gaps, and work with partners to improve data and processes.
Partner with APM Operations Leadership to improve 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 their contexts.
Work across organizational boundaries to obtain information, reconcile differing definitions, and balance functional needs with enterprise consistency.
Build trust with technical and non-technical stakeholders, explain analytical choices, and deliver useful work with limited oversight.
Contribute reusable standards, methods, templates, and examples to an enterprise analytics community.