Manager, Data Science

At Hill’s we have a purpose. Every day around the world, we transform the lives of millions of pet families through pioneering innovation, amazing nutrition, and the best and brightest people. If you’re interested in work that matters, fueled by passion for pets, we invite you to apply. Founded more than 75 years ago with an unwavering commitment to pet nutrition, Hills’ mission is to help enrich and lengthen the special relationships between people and their pets. Our decades of science and research guide us in creating nutrition that’s a step ahead so pets and pet parents can enjoy every day together. As the US #1 Veterinarian Recommended pet food brand, knowledge is our first ingredient with 220+ veterinarians, PhD nutritionists and food scientists working to develop breakthrough innovations in pet health.

Role Summary

The Manager, Data Science leads the end-to-end development, deployment, operationalization, and optimization of scalable advanced analytics, machine learning, and Generative AI solutions for the global marketing and sales organization while leveraging scalable data products. Reporting directly to the Director, Global Data Science, this role acts as a strategic analytics translator and change agent, bridging technical data science capabilities with commercial goals. Leveraging expertise to utilize cloud infrastructure, robust MLOps practices, and quantitative modeling, the Manager drives data-informed decision-making that delivers quantified value realization, directly accelerating global sales growth and pet care innovation.

Responsibilities

• Adopt a data product mindset to apply and utilize scalable data products within advanced analytics, defining, framing, and prioritizing complex commercial challenges into high-impact analytics roadmaps while serving as an analytics translator and advocate for data-driven decision-making to drive quantified value realization (e.g., direct P&L impact, measurable revenue uplift, and automated efficiency gains).

• Lead the end-to-end development, deployment, operationalization, and lifecycle management of predictive, machine learning, and Generative AI / Large Language Models (LLMs) to solve critical sales and marketing challenges across workflows for unstructured data, feature engineering, and automated insight generation.

• Help lay the foundation for MLOps practices, CI/CD pipelines, model monitoring, and governance frameworks to support model performance, reliability, and security as our AI/ML capabilities mature.

• Design, execute, and evaluate continuous experimentation protocols, including A/B testing and causal inference, to measure and improve commercial outcomes.

• Partner with cross-functional commercial leaders, external vendors, and IT/data engineering teams to leverage enterprise cloud data infrastructure (AWS/Azure) and ML tooling as we mature our cloud environment.

• Build and automate interactive reporting dashboards using platforms such as Tableau, Domo, or Google Data Studio to deliver actionable insights to non-technical stakeholders.

• Drive change management and user adoption strategies, partnering closely with business stakeholders to transition AI capabilities into everyday commercial operations.

Required Qualifications

• Bachelor’s Degree in Data Science, Statistics, Computer Science, Business Analytics, or a related quantitative field.

• 5+ years of experience in applied data analytics, data science, quantitative programming, or a related field.

• Hands-on proficiency with SQL and Python or R for statistical modeling and predictive analytics.

• Experience designing and executing A/B testing frameworks and applying modern Generative AI techniques, Large Language Models (LLMs), and prompt engineering/fine-tuning in enterprise settings.

• Proven experience utilizing enterprise cloud platforms and data infrastructure (e.g., AWS, Azure, Google Cloud Platform, Snowflake).

• Familiarity with SAP, customer analytics within CPG/Retail.

Preferred Qualifications

• Master’s Degree in a quantitative discipline.

• Demonstrated ability to manage ambiguity, lead project timelines independently, apply change management techniques to drive user adoption, and translate complex technical findings to non-technical executive audiences.

• Proven track record of operating with a data product mindset and delivering measurable ROI and quantified value realization across commercial analytics initiatives.

• Familiarity with model deployment, integration, and foundational MLOps principles using modern data architecture, containerization, and workflow orchestration tools to support model stability and monitoring.