Machine Learning Engineer

Colgate-Palmolive is building production-ready machine learning capabilities within its Enterprise AI/ML Center of Excellence. This onsite role in New York focuses on moving high-priority ML work from research into reliable services, with strong emphasis on orchestration, statistical validation, and MLOps practices.

As a Machine Learning Engineer, you will help architect and deliver compliant solutions that can operate in production, supported by disciplined pipelines and developer tooling across the ML and software lifecycle.

What you will do

• Productionize machine learning research by converting experimental models into robust, scalable production services, including the supporting pipeline.
• Orchestrate data and ML pipelines using Airflow and dbt to maintain data integrity and improve model reliability.
• Apply statistical rigor through advanced statistical modeling and hypothesis testing to validate model performance with outcomes that are testable and trustworthy.
• Support DevOps and MLOps by working within CI/CD and software lifecycle management processes for both ML and engineering deliverables.

Minimum 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: 6+ years of technical experience with a bachelor’s degree; 3+ years if you hold a Master’s or PhD.

Technologies you will work with

• Airflow, dbt
• Python, SQL, Scikit-learn
• Docker, Kubernetes
• CI/CD, Git

Additional benefits

• Comprehensive benefits package including medical, dental, vision, and basic life insurance.
• Paid parental leave.
• Disability coverage.
• 401(k) retirement plan participation with company matching contributions, subject to eligibility requirements.
• Minimum 15 vacation/PTO days (hourly employees receive a minimum of 120 hours).
• 13 paid holidays (vacation days prorated based on hire date within the calendar year).
• Paid sick leave adjusted based on role and location in accordance with local laws.

Preferred qualifications

• Proven expertise in Data Science and/or Machine Learning Engineering.
• Advanced production-grade proficiency in Python and SQL.
• Hands-on experience with Airflow orchestration and dbt transformations.
• Familiarity with modern IDEs and agentic coding systems (for example, Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.
• Expert knowledge of modern Python and Scikit-learn plus major ML libraries.
• Deep understanding of the data lifecycle (ETL/ELT), data architecture, and best practices for templatized data transformation.
• Experience with Docker/Kubernetes, CI/CD, Git, and “Software Engineering for ML” best practices.
• LLM literacy, including concepts underpinning LLMs and strategies to integrate GenAI into the MLE project lifecycle.

Location: New York, NY (onsite) | Compensation: USD 130,000 - 170,000 per year