Manager, Data Science

Manager, Data Science

The Manager of Data Science will build and lead a focused, high-impact team solving complex, high-value business problems through applied data science. This role defines how data science is used to improve how the business operates and makes decisions.

Operating in close partnership with business units, Central Analytics, and Data Engineering, this team functions as a high-leverage strike team, deploying into targeted, time-bound efforts (typically 8–24 weeks) to connect signals across the business and deliver measurable impact across cost, risk, and operational performance.

This role sets technical direction and ensures the team applies sound statistical and machine learning practices, while remaining grounded in real-world outcomes. The Manager is expected to stay hands-on, partially contributing to feature engineering, model development, evaluation, and production readiness to ensure solutions are not only technically sound, but usable and durable in practice.

Success in this role requires balancing analytical insight with operational reality, combining data with the experience and intuition of teams in the field to identify risks earlier, improve planning, and enable better decisions. This leadership role lives within the Digital Operations organization with responsibility for building and shaping the data science capability from the ground up as McGough continues to scale its investment in data, technology, and analytics.

Required:

• Bachelor's degree in Data Science, Statistics, Mathematics, Engineering, or related field
• 6–10+ years of experience in data science, advanced analytics, or applied modeling
• Proven experience building statistical or machine learning models and delivering them in real-world business contexts with measurable outcomes
• Strong programming experience in Python (or equivalent), including data manipulation, modeling, and evaluation
• Experience leading or mentoring analytical teams
• Experience working with version control (e.g., Git) and structured development practices
• Strong communication skills translating technical outputs into business decisions

Preferred:

• Master's degree in Data Science, Statistics, Mathematics, Engineering or related field.
• Experience in complex operational environments (construction, manufacturing, logistics, etc.)
• Experience selecting, building, and evaluating machine learning models across multiple problem types
• Experience with optimization, simulation, or advanced forecasting
• Familiarity with modern data platforms and engineering concepts
• Experience applying machine learning in real-world business settings

Skills:

• Strong problem structuring and analytical reasoning
• Ability to operate effectively with incomplete or imperfect data
• Experience with statistical modeling, machine learning, or optimization techniques
• Experience with model validation, feature engineering, and performance evaluation
• Ability to design reproducible analytical workflows and structured development practices
• Strong Python or equivalent analytical tooling proficiency
• Clear communication of complex concepts into actionable decisions
• Ability to balance analytical rigor with practical application

Problem Framing & Solution Design

• Translate loosely defined business challenges into structured analytical problems
• Define success criteria tied to business decisions and measurable outcomes
• Determine appropriate approaches including forecasting, optimization, or modeling
• Identify key assumptions, constraints, and risks early

Advanced Analytics Delivery

• Lead development of predictive models, scenario analysis, and decision frameworks
• Guide team through ambiguous data environments without stalling on perfection
• Ensure outputs are actionable, interpretable, and aligned to business use
• Guide model evaluation, validation, and performance monitoring practices
• Ensure models are designed for production use, including scalability, robustness, and maintainability
• Accountable for the full analytical lifecycle from problem framing through model development, validation, deployment readiness, and post-deployment performance tracking
• Ensure analytical outputs are reproducible,…