Manager, Data Science

Qualifications

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

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

Core Responsibilities

• 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, well-documented, and stable enough for business use beyond initial delivery
• Operating Model & Intake Discipline
• Define and enforce intake criteria focused on high-value, non-routine problems
• Prioritize work based on business impact rather than request volume
• Manage project-based work cycles (8–24 weeks) with clear start and end points
• Ensure completed work is transitioned to BI, Data Engineering, or business teams for ongoing use
• Model Development & Production Readiness
• Guide development of models and analytical workflows that can be reused or extended beyond one-time analysis
• Establish lightweight practices for versioning, validation, and documentation of analytical work
• Ensure clear ownership and transition plans for models after delivery (handoff to BI, Data Engineering, or business teams)
• Define when analytical solutions require further operationalization versus remaining project-based
• Team Leadership
• Build and lead a small team of advanced analysts or data scientists
• Act as a player-coach, contributing directly to complex analytical work
• Set standards for analytical rigor, clarity, and business relevance
• Develop team capability in both technical and business-facing skills