Manager, Machine Learning Engineering

No Relocation Assistance Offered

Job Number #171681 - New York, New York, United States

Colgate-Palmolive Company is a global consumer products company operating in over 200 countries specializing in Oral Care, Personal Care, Home Care, Skin Care, and Pet Nutrition. Our products are trusted in more households than any other brand in the world, making us a household name!

Join Colgate-Palmolive, a caring, innovative growth company reimagining a healthier future for people, their pets, and our planet. Guided by our core values—Caring, Inclusive, and Courageous— we foster a culture that inspires our people to achieve common goals. Together, let's build a brighter, healthier future for all.

The Manager, Machine Learning Engineering is a technical leader who bridges the gap between enterprise AI strategy and production grade execution. In alignment with Colgate-Palmolive’s purpose to Make More Smiles and our commitment to a healthier future for our people, pets, and planet, this role ensures that advanced machine learning capabilities are translated into scalable, responsible solutions that deliver measurable business impact across our global operations.

As part of the Enterprise AI/ML Center of Excellence, you will lead the architectural design and end-to-end execution of high-priority ML initiatives. This involves integrating statistical modeling, optimization, and autonomous workflows into Colgate-Palmolive's business processes to accelerate innovation, enhance decision intelligence, and embed AI. Beyond hands‑on technical work, you ensure solutions are architecturally sound, production‑ready, and compliant with enterprise governance standards, translating strategy into robust execution aligned with stakeholder needs and long‑term value creation.

• Architectural Design:
  Lead the design of scalable, modular ML architectures. Define how models, data pipelines (Airflow/dbt), and inference services integrate with enterprise applications and cloud infrastructure (GCP).
• Project Management:
  Own the full ML lifecycle for key enterprise work streams. Manage resource allocation, sprint planning, and delivery timelines to ensure "moonshot" projects move from lab to production.
• Stakeholder Management:
  Act as the primary technical point of contact for business partners in Marketing, Supply Chain, and Finance. Translate complex business problems into technical requirements and communicate project impact through data‑driven narratives.
• Production Excellence:
  Oversee the transition of experimental research into robust production services. Ensure all systems meet enterprise standards for MLOps, security, and reliability.
• Technical Governance:
  Ensure that architectural patterns and coding standards are followed across the team, conducting design reviews and code audits to maintain "Software Engineering for ML" best practices.

• Bachelor’s Degree (or higher) in a high‑rigor field:
  Statistics, Data Science, Computer Science, Physics, or Mathematic
• Industry
  
  Experience:
  
  6+ years of experience in Data Science or ML Engineering, with at least 2 years in a project leadership or supervisory capacity.
• Architecture & Systems:
  Proven track record of designing and deploying deeply integrated ML use cases that span multiple business workflows.
• Stack Expertise:
  Expert‑level proficiency in Python (production‑grade), SQL, and GCP. Hands‑on experience architecting pipelines with Airflow and dbt.
• Velocity Tools:
  Advanced proficiency with Agentic Coding systems (e.g., Cursor, Windsurf) to accelerate the project lifecycle.
• System Design:
  Deep understanding of MLOps, containerization (Docker/Kubernetes), and CI/CD frameworks.
• Statistical Depth:
  Mastery of Bayesian methods, causal inference, and predictive modeling techniques to ensure model validation.
• Modern AI Integration:
  Ability to design strategies that integrate LLMs and Generative AI into traditional ML workflows (e.g., using LLMs to automate processes).
• Data Engineering:
  Expertise in data lifecycle management (ETL/ELT) and templatized data transformation.

• Project Leadership:
  Mastery of Agile/Scrum methodologies for…