Manager Data Science (930982)

Equus•Louisville, KY

About The Position

The Manager, Data Science is a hands-on member of the Enterprise Performance & Governance team. The role translates complex and fragmented data into clear insights, practical tools, and action to support enterprise performance priorities and analytical projects across Finance, Procurement, Legal, Operations, and other corporate and business teams. The position combines coding, statistical methods, business intelligence, data visualization, and responsible use of artificial intelligence to investigate business questions, improve recurring analyses, automate manual work, and develop better solutions to enterprise problems. The manager is expected to advance ambiguous work with minimal direction and communicate findings in a form that leaders and functional partners can use. The role develops advanced analyses and predictive or prescriptive insights; creates and maintains Power BI dashboards and analytical products; prepares and analyzes data from multiple sources; and builds reusable workflows and automation. It also partners across functions to improve performance visibility, data quality, metric consistency, and analytical practices, and contributes reusable standards, methods, templates, and examples to an enterprise analytics community. Success means producing trusted, documented analyses and useful tools that make performance, risks, and opportunities easier to understand and act on, while progressing promising analytics and AI approaches into repeatable business value. The position has an estimated travel requirement of 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.
Demonstrated experience using coding and query languages for data analysis, including strong proficiency in at least one general-purpose analytical language such as Python or R and working proficiency in SQL.
Hands-on experience developing dashboards and analytical solutions in Microsoft Power BI, 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, identify appropriate data and methods, and 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.
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 confidence in conclusions.
Use AI tools to accelerate research, coding, data preparation, analysis, documentation, and problem solving.
Identify opportunities to embed AI, automation, or advanced analytics into enterprise and functional workflows; prototype approaches and turn successful concepts into scalable, maintainable solutions.
Stay current on developments in data science, AI, analytics engineering, and visualization, and apply relevant developments to business problems.
Design, build, and maintain Power BI dashboards, scorecards, and analytical products for enterprise, business-unit, contract, and functional performance.
Develop visualizations and executive-ready analyses that communicate trends, exceptions, relationships, risks, business impact, and recommended action.
Partner with stakeholders to define metrics, business rules, thresholds, and reporting logic, supporting consistent definitions across the organization.
Use coding and query languages such as Python, SQL, M, DAX, and Power Query 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.
Assess data quality, completeness, and usability; identify gaps and work with partners to improve underlying data and processes.
Partner with APM Operations Leadership on 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 business 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 by asking strong questions, explaining analytical choices, and delivering useful work with limited oversight.
Contribute reusable standards, methods, templates, and examples to an enterprise analytics community.