Partner with Global Supply Chain leadership and subject-matter experts to define analytical problem statements, clarify assumptions and constraints, and determine appropriate statistical or modeling approaches
Perform exploratory data analysis and statistical analysis to identify patterns, drivers, and risks in supplier performance and related supply chain data
Design, develop, and deploy machine learning and advanced analytics solutions that support supplier performance, risk identification, and decision-making
Translate analytical outputs into user-facing, production-grade ML and AI applications (for example, Streamlit or Gradio tools) that make insights accessible to non-technical users
Bridge analytics and infrastructure by building systems that operationalize models and analyses through scalable data pipelines and applications
Develop and maintain cloud-based infrastructure and tooling using platforms such as AWS and Databricks to support reliable analytics workflows
Design and implement CI/CD pipelines, infrastructure-as-code, and MLOps practices that support model deployment, monitoring, and iteration
Improve existing workflows and advocate for strong software engineering and analytical best practices, including version control, testing, documentation, and reproducibility
Stay current on statistical methods, machine learning techniques, and analytics tooling, applying them where they deliver measurable business value

Must have a PhD with 4 years of relevant professional experience, OR a Master’s degree with 6 years of relevant professional experience, OR a Bachelor’s degree with 8 years of relevant professional experience
Must have a strong proficiency in Python, SQL, and Git
Must have a solid foundation in statistical analysis, data exploration, and applied modeling
Must have experience building and deploying machine learning or advanced analytics solutions
Must have experience with rapid application development frameworks such as Streamlit, Gradio, Starlette, or Next.js
Must have working knowledge of DevOps or MLOps concepts as applied to data science workflows
Must have experience with containerization technologies such as Docker or Podman
Must have the ability to collaborate effectively with data scientists and analysts on analytical methods and results

health insurance coverage
life and disability insurance
savings plan
Company paid holidays and paid time off (PTO) for vacation and/or personal business

Senior Principal Data Scientist

Key skills