Machine Learning Engineer

*This role can sit in our Park Ave (NYC) or Piscataway, NJ office*

Role Summary

We are seeking a Machine Learning Engineer who brings the analytical rigor of a data scientist and the engineering discipline of a software architect. In support of Colgate-Palmolive’s purpose to Make More Smiles and our commitment to a healthier future for our people, pets, and planet, this role builds the advanced machine learning capabilities that power smarter decisions, accelerate innovation, and create measurable impact across our global enterprise.

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.

Responsibilities:

• Productionize ML Research: Transition experimental models into robust, scalable production services. You don't just build the model; you build the pipeline that sustains it.

• Pipeline Orchestration: Design and maintain complex data and ML pipelines using Airflow and dbt to ensure data integrity and model reliability.

• Statistical Rigor: Apply advanced statistical modeling and hypothesis testing to validate models, ensuring outcomes are testable and honest.

• DevOps & MLOps: Utilize modern developer tools to work within and CI/CD frameworks for ML and software lifecycle management

Required Qualifications:

• Bachelor’s Degree (or higher) in a high-rigor field: Statistics, Physics, Chemistry, Mathematics, Data Science, or Computer Science with a heavy emphasis on Statistical Learning.

• Experience: Bachelors degree: 6+ of years of technical experience; Masters or PhD (3+ years)

Preferred Qualifications:

• Proven expertise in Data Science and/or Machine Learning Engineering.

• Advanced proficiency in Python (Production-grade) and SQL.

• Hands-on experience with Airflow for orchestration and dbt for transformation.

• Familiarity with modern IDEs and Agentic Coding systems (e.g., Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.

• Modern Stack: Expert knowledge of Python, Scikit-learn, major ML Libraries

• Data Engineering: Deep understanding of data lifecycle (ETL/ELT), data architecture, best practices for templatized data transformation

• Engineering Excellence: Familiar with Docker/Kubernetes, CI/CD, Git, and "Software Engineering for ML" best practices.

• LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle