Senior Data ScientistActive

Lucid Motors
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The opportunity

At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing™” approach to the global automotive market.

What you'll do

  • Predictive Quality: Build and refine statistical and machine learning models that identify emerging quality concerns earlier and help teams prioritize action before issues scale across the field.

  • Risk Modeling: Create frameworks that quantify warranty exposure, failure risk, component reliability, and customer impact so engineering teams can make resource decisions based on technical and business risk.

  • Root Cause Insights: Lead data-driven investigations using warranty, service, telematics, and diagnostic data to identify patterns, validate hypotheses, and support root cause determination.

  • Automation: Develop automated dashboards, alerts, tools, and model-driven: workflows that improve fleet health monitoring, issue detection, and quality performance visibility.

  • Data Readiness: Partner with Data Engineering to define, improve, and maintain reliable data pipelines that support accurate, scalable, and business-critical Field Quality analytics.

  • Decision Support: Translate complex analytical findings into clear recommendations that inform quality reviews, escalation decisions, corrective action prioritization, and strategic planning.

What they're looking for

  • Methodology Standards: Establish modeling approaches, statistical standards, and best practices that strengthen data-driven decision making across the Field Quality organization.
  • years of experience in data science, machine learning, applied statistics,: reliability analytics, or a related quantitative discipline.
  • Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering,: Mathematics, or a related technical field.
  • Proficiency in Python, SQL, statistical modeling, and common data science: libraries such as Pandas, NumPy, and Scikit-Learn.