Senior Data Scientist

Lucid Motors•Phoenix, AZ

13d•Onsite

About The Position

About Lucid 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. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience. We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility. About the Team Field Quality helps Lucid turn real-world vehicle data into action that improves reliability, serviceability, and the customer experience. The team connects inputs from warranty claims, service repairs, telematics, diagnostic logs, customer feedback, and vehicle usage to identify patterns, prioritize risks, and guide corrective action. What makes this team unique is its position at the intersection of data science, engineering, service operations, and manufacturing, where insights directly influence vehicle quality decisions. By building scalable analytics and predictive capabilities, the team helps Lucid address quality concerns earlier and strengthen long-term vehicle performance. About the Role As a Senior Data Scientist, Field Quality , you will report to a Field Quality leadership role and own the development of predictive models, analytical frameworks, and scalable data solutions that help Lucid identify and resolve vehicle quality issues sooner. This role exists to close the gap between high-volume field data and timely, data-driven quality decisions across warranty, service, reliability, product engineering, manufacturing, and data engineering teams. In your first 12 months, you will deliver predictive quality models, establish repeatable analytics for risk-based prioritization, and enable faster decisions that improve vehicle reliability and reduce warranty exposure. Success will be measured by earlier issue detection, improved prioritization of corrective actions, stronger visibility into fleet health, and adoption of your analytics by cross-functional stakeholders. A strong candidate will gain the opportunity to build Field Quality analytics capabilities in a growing EV environment where their models can directly connect customer experience to engineering action. What You Own 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. Methodology Standards: Establish modeling approaches, statistical standards, and best practices that strengthen data-driven decision making across the Field Quality organization.

Requirements

5 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.
Experience developing, validating, and deploying statistical or machine learning models using large, complex datasets.
Ability to read, write, speak, and understand English sufficiently to perform the essential duties of the position, including understanding work instructions, safety procedures, and job-related communications.

Nice To Haves

Experience analyzing automotive warranty, quality, reliability, service, telematics, or connected vehicle data.
Familiarity with warranty forecasting, survival analysis, cost-per-vehicle modeling, fleet health monitoring, or anomaly detection.
Experience with Airflow, Databricks, Spark, Snowflake, Tableau, Power BI, or cloud-based data ecosystems.
Knowledge of vehicle diagnostic data, DTCs, CAN signals, OTA systems, EV technologies, or reliability engineering methodologies.
Experience supporting root cause investigations, corrective action effectiveness evaluations, or quality management systems in automotive, aerospace, manufacturing, or another regulated environment.

Responsibilities

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.
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.
Lead data-driven investigations using warranty, service, telematics, and diagnostic data to identify patterns, validate hypotheses, and support root cause determination.
Develop automated dashboards, alerts, tools, and model-driven workflows that improve fleet health monitoring, issue detection, and quality performance visibility.
Partner with Data Engineering to define, improve, and maintain reliable data pipelines that support accurate, scalable, and business-critical Field Quality analytics.
Translate complex analytical findings into clear recommendations that inform quality reviews, escalation decisions, corrective action prioritization, and strategic planning.
Establish modeling approaches, statistical standards, and best practices that strengthen data-driven decision making across the Field Quality organization.