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Tier-1 brand, generalist Data Scientist title, and metro location increase applicant competition.
Core data and ML skills are transferable, but automotive systems experience requirement increases domain specificity.
Requires specific tech stack and automotive domain experience but lacks explicit years, yielding medium strictness.
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Design and develop analytics solutions including statistical analysis, data visualizations, and custom data models for efficiency improvement in simulation workflows.
Analyze customer vehicle data to find insights and lead data-driven root cause analysis.
Collaborate with key stakeholders globally to solve engineering problems using data science techniques.
Proficiency in SQL and Python, and experience with visualization tools like PowerBI, Tableau, or Qlickview.
Experience in data modelling, machine learning, and statistical analysis as part of problem solving.
Bachelor's or Master's degree in any engineering stream with product development experience in Automotive, Aerospace, Auto & Aero Tier 1 supplier, or heavy engineering companies.
Work Experience Required: Not explicitly mentioned in the JD but career break of minimum one year indicates prior experience; knowledge of Git workflow is required.
Experienced in automotive systems; knowledge of Powertrain systems is a plus, indicating domain familiarity that aligns with problem scope.
Ability to operate within global, agile, and cross-functional teams collaborating across multiple sites.
Able to restart career after a break of 1 year or more with relevant prior engineering and data science background, suited for the ExcelHer returnship program.