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Mid-level, popular Data Scientist role with broad toolset and generalist requirements increases candidate competition.
Preference for manufacturing, HSE, supply chain and finance domain awareness makes background fit highly sensitive.
Mandatory 5+ years plus specific AI/ML, platform, and deployment experience implies high shortlisting strictness.
Designs, develops, and deploys enterprise-wide AI/ML/gen-AI digital solutions including requirements gathering, exploratory data analysis, programming, modeling, and algorithms.
Leverages analytics platforms such as Databricks, Palantir, or Snowflake to solve business problems and operationalize models across manufacturing, HSE, supply chain, and finance domains.
Collaborates with domain experts to validate models and clearly communicates statistical results and methodologies to stakeholders.
Minimum 5 years of experience as a data engineer and/or data scientist.
Proficiency with analytics platforms including Databricks, Palantir, Snowflake, or equivalents.
Demonstrated experience in full lifecycle AI/ML solution development and deployment (from requirements through MLOps).
Education: College or university degree in a relevant technical discipline or equivalent experience.
Experienced in building and managing analytical solutions in cloud environments such as Microsoft Azure and/or AWS with big data toolsets.
Strong domain knowledge in manufacturing operations, health, safety & environment (HSE), supply chain, and finance.
Skilled in Agile software development, CI/CD practices, and familiar with validation/testing of machine learning systems.