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Tier-1 employer, metro location, and broad analytics/AI skillset drive high candidate competition.
Preferred supply chain and life sciences analytics experience moderately limits cross-industry transferability.
Multiple required technical and domain skills increase screening rigor despite no explicit years requirement.
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Develop reusable, scalable data science and analytics solutions supporting supply chain, manufacturing, planning, logistics, clinical supply, and operations decision-making.
Prepare, integrate, and transform complex datasets from multiple sources to enable reliable analytics, modeling, and visualization.
Collaborate with product owners, data engineers, and technology teams to translate business needs into data product requirements and enable analytical applications through cloud platforms and modern deployment techniques.
Master’s degree or PhD in Data Science, Computer Science, Statistics, Operations Research, Engineering, Supply Chain Analytics, Applied Mathematics, or related quantitative field.
Strong programming skills in Python or R and strong SQL skills for data preparation and analysis.
Experience with cloud data platforms like Databricks or Snowflake, including data preparation and scalable analytics development.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in supply chain, manufacturing, planning, logistics, clinical supply, life sciences, or operations analytics domains applying data science techniques to complex business problems.
Capable of moving analytical workflows beyond exploratory notebooks to maintainable, governed, and reusable solutions within cloud environments.
Effective at partnering across technical and business teams to frame analytic questions, translate requirements, and communicate actionable insights clearly.