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Tier-1 employer, metro location, and a generalist senior data scientist role increase applicant competition.
Core data science skills transfer, but supply chain and life-sciences domain experience increases sensitivity.
Strong required data platform, programming, and domain experience indicate strict technical and domain filters.
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Develop reusable, scalable data science and analytics products supporting supply chain, manufacturing, planning, logistics, clinical supply, and operations decision-making.
Prepare and integrate complex datasets from multiple sources using cloud platforms like Databricks or Snowflake to enable scalable analytics and reusable workflows.
Collaborate with cross-functional teams including product owners, data engineers, and technology teams to translate business needs into data product requirements and support delivery of analytical tools via modern application patterns.
Master’s degree or PhD in Data Science, Computer Science, Statistics, Operations Research, Engineering, Supply Chain Analytics, Applied Mathematics, or related quantitative field.
Proven experience in data science application including statistics, forecasting, simulation, optimization, machine learning, or visualization addressing complex business problems.
Strong programming skills in Python or R and strong SQL skills for data preparation and analysis.
Experience with cloud data platforms such as Databricks or Snowflake for preparing large datasets and developing scalable analytics workflows.
Experienced in supply chain, manufacturing, planning, logistics, clinical supply, or operations analytics within life sciences or related domains.
Capable of advancing analytical workflows beyond notebooks into governed, maintainable, reusable solutions with rigorous documentation and version control.
Skilled in partnering with diverse stakeholders to frame analytical challenges, define requirements, and communicate quantitative insights effectively to technical and non-technical audiences.