Role overview
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 our Global AI/ML team, the Enterprise Center of Excellence for applied AI and ML research, you will design and deploy deeply integrated ML solutions that connect data, models, and business workflows. You will build the statistical, machine learning, and optimization models that enable autonomous workflows and internally developed end user applications, helping shift AI from isolated experimentation to embedded enterprise value.
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
Basic 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. Optional: Ph.D in a quantitative field
- Experience: Bachelors degree: 3+ of years of technical experience; Masters or PhD (1+ years)
- 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.
- Statistical Depth: Proficient with a selection of Bayesian methods, causal inference, and/or predictive modeling techniques
- LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle
Preferred qualifications
- Problem-solving: Ability to decompose complex business challenges into actionable AI solutions
- Communication: Ability to explain technical concepts to non-technical stakeholders
- Analytical Thinking: Ability to assess business impact of projects and prioritize accordingly
- Collaboration: Experience working effectively with cross-functional teams and stakeholders
- Project Management: Ability to manage multiple initiatives simultaneously while meeting deadlines
- Narrative Thinking: Ability to back data with a compelling business story for stakeholders.
- Experience in a global, matrixed environment (FMCG/Manufacturing preferred).