Post Holdings-posted about 1 month ago
$102,931 - $152,338/Yr
Full-time • Mid Level
Hybrid • Lakeville, MN
5,001-10,000 employees
Food and Beverage Retailers

Are you a strategic thinker who thrives on solving complex business challenges with data? Do you see opportunities where others see noise? At Post Consumer Brands (PCB), we're looking for a Manager of Data Science to lead our Decision Science team in transforming data into actionable insights and next-generation solutions. This high-impact role will guide a team of Data Scientists and Machine Learning Engineers, collaborate across departments, and influence senior leaders to embrace data-driven innovation. You'll be at the forefront of building scalable solutions that drive revenue, optimize operations, and fuel a culture of continuous learning and improvement.

  • Own end-to-end delivery of high-quality machine learning, generative AI, and advanced analytics solutions aligned with strategic objectives.
  • Engage business leaders to understand challenges and recommend data-driven solutions.
  • Lead advanced analytics projects, uncover insights, and deliver business impact.
  • Communicate data stories effectively across all levels-from developers to C-suite.
  • Develop scalable solutions for key functions like Manufacturing, Supply chain, and Marketing.
  • Drive change management initiatives to build buy-in among divisions and senior leaders for the new internal practice.
  • Lead the transition of Data Science and AI capabilities from Post Holdings (PHI) to PCB, ensuring continuity, integration of existing assets and processes, and documentation.
  • Evangelize the use of Data Science and AI across Post Holdings.
  • Lead user groups and adoption initiatives to spread knowledge and excitement.
  • Understand business decision-making processes and embed data-driven practices.
  • Drive enterprise-wide use of Data Science assets and ensure contextual delivery.
  • Establish and scale PCB's internal Data Science and AI practice, including knowledge transfer from PHI and development of PCB-specific methodologies.
  • Stay ahead of emerging technologies in Data Science and Generative AI.
  • Maintain a pipeline of ideas to generate value from data.
  • Lead proof-of-concept projects from problem definition to implementation.
  • Simplify architecture and tool selection through elegant design.
  • Mentor and coach team members to grow autonomy and leadership.
  • Share best practices and foster a culture of continuous improvement.
  • Align team capabilities with strategic goals and individual growth paths.
  • Build and release AI/ML templates to empower data-driven productivity.
  • Design and implement onboarding and upskilling programs for new team members to accelerate PCB's self-sufficiency.
  • Manage resources and prioritize projects to align with company strategy.
  • Oversee ML Ops, model deployment, and post-mortem analyses.
  • Ensure reliability, scalability, and continuous improvement of models and solutions.
  • Define and implement governance, best practices, and standards for Data Science and AI at PCB.
  • Develop and track KPIs to measure progress toward self-sufficiency and practice maturity.
  • 7-10 years of experience in Data Science, data engineering, or applied analytics.
  • Bachelor's degree in a quantitative field; advanced degree preferred.
  • 4+ years of experience with BI tools (Power BI, Tableau, etc.).
  • 2+ years of experience managing people and projects.
  • 4+ years of experience in Python and software development best practices.
  • Experience with cloud ML platforms (Azure ML preferred).
  • Strong communication skills and a passion for data-driven problem solving.
  • 5+ years in consumer-packaged goods (CPG), with deep knowledge in sales, marketing, supply chain, or manufacturing.
  • 3+ years in data engineering (ETL/ELT, SQL, Informatica, dbt).
  • Familiarity with ERP systems (JDE, Oracle, SAP).
  • Strong analytical, cognitive, and problem-solving skills.
  • Ability to work with ambiguity and adapt to change.
  • Experience managing competing priorities using Agile and Waterfall methods.
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