Sr. Principal Data Scientist

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 You are comfortable moving between data analysis, modeling, application development, and infrastructure depending on the problem. You translate between data scientists, engineers, and supply chain leaders, helping align analysis, systems, and decisions. 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 Strong problem-solving and critical-thinking skills Clear communication skills and comfort engaging with non-technical stakeholders Demonstrated experience deploying, operating, and monitoring production analytics or ML systems on AWS Experience with Databricks and PySpark for data transformation, feature engineering, and analytics Experience building and deploying analytical or decision-support tools for business users Familiarity with workflow orchestration tools such as AWS Step Functions or Apache Airflow Experience with infrastructure-as-code tools including Terraform or AWS CloudFormation Understanding of scalable data architectures and model lifecycle management Domain experience in supply chain analytics, manufacturing analytics, operations research, or financial forecasting Background in consulting or other client-facing technical roles where statistical analysis and modeling were used to inform decisions under ambiguity