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Strong employer brand but senior, niche supply-chain life-sciences focus reduces broad applicant pool.
Strong supply-chain and life-sciences domain requirements reduce cross-industry transferability.
Multiple technical and domain skills expected despite no explicit years requirement.
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Develop and deliver reusable, scalable analytics and data science solutions across supply chain, manufacturing, planning, logistics, clinical supply, and operations domains.
Prepare, clean, transform, and integrate complex datasets from multiple sources for analysis, modeling, and decision support.
Collaborate with product owners, data engineers, and technology teams to translate business needs into data product requirements and enable analytical applications via modern platforms and APIs.
Master’s or PhD in Data Science, Computer Science, Statistics, Operations Research, Engineering, Supply Chain Analytics, Applied Mathematics, or related quantitative field.
Experience applying data science techniques such as forecasting, simulation, optimization, machine learning, or visualization to complex business problems.
Strong programming skills in Python or R and SQL, with experience in Databricks, Snowflake, or similar cloud data platforms.
Work Experience Required: Not explicitly mentioned in the JD.
Experience in supply chain, manufacturing, planning, logistics, clinical supply, life sciences, or operations analytics domains, with strong data product and solution delivery focus.
Proficient in data engineering practices including ETL/ELT, data modeling, pipeline development, and building governed, reusable analytical workflows beyond notebooks.
Ability to partner effectively with cross-functional teams to frame analytical questions and deliver insights through scalable, maintainable solutions and decision-support tools.