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Tier-1 employer, metro location and broad mid-level data scientist role drive high applicant competition.
Core data science skills transferable, but supply chain and life-sciences domain experience increases hiring sensitivity.
Requires specific tech stack, domain knowledge, and senior title but no explicit years, so moderate strictness.
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Develop and deliver reusable, scalable analytics and data science solutions for global supply chain, manufacturing, planning, logistics, clinical supply, and operations challenges.
Prepare, clean, transform, and integrate complex datasets from multiple sources, leveraging cloud data platforms like Databricks or Snowflake.
Partner with business, technology teams, and digital product owners to translate needs into data product requirements and support analytic decision-making through modern applications or APIs.
Advanced degree (Master’s or PhD) in Data Science, Computer Science, Statistics, Operations Research, Engineering, Supply Chain Analytics, Applied Mathematics, or related quantitative field.
Proficiency in Python or R programming along with strong SQL skills for data integration and analysis.
Experience with cloud data platforms such as Databricks, Snowflake, or similar for scalable analytics and reusable workflow development.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in applying data science, machine learning, forecasting, simulation, or optimization methods to supply chain or operations problems, preferably in life sciences or related sectors.
Demonstrated ability to develop maintainable, governed analytic workflows moving beyond notebooks into production solutions, with knowledge of ETL/ELT and data engineering practices.
Effectively bridges business and technical teams, translating complex analytical outputs into clear, actionable recommendations and decision-support tools.