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Strong employer brand, metro location, and mid-level ML role increase applicant competition.
Requires PLM/ERP and engineering domain experience, limiting cross-industry transferability.
Explicit 6+ years plus required ML, Databricks, and PLM/ERP integration skills raise strictness.
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Design, develop, and deploy advanced analytics and AI models to analyze large-scale engineering, PLM, and BOM datasets for early identification of product, part, and lifecycle risks.
Lead end-to-end data science projects including data discovery, model development, deployment, and monitoring using Databricks and Spark-based platforms.
Collaborate with cross-functional teams and mentor junior data scientists to shape data-driven decision-making and analytics governance for product readiness and risk optimization.
6+ years of full-time experience as a Data Scientist or in an equivalent analytics role.
Postgraduate degree in Data Science, Computer Science, Statistics, Engineering, or related field.
Proficiency in Python and strong SQL skills; hands-on experience with Databricks, Apache Spark, and working with large enterprise datasets.
Experience integrating and analyzing data from Teamcenter PLM and SAP ERP systems; strong experience building dashboards with Tableau.
Experienced in applying statistical modeling, machine learning, and AI in an enterprise engineering or manufacturing environment, especially involving PLM and ERP data.
Capable of leading analytics initiatives from pilot to enterprise-wide scale while collaborating with senior engineering and manufacturing stakeholders.
Strong technical skills in big data platforms and dashboard development combined with ability to translate complex datasets into actionable insights for cross-functional teams.