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Senior Data Scientist – Vehicle Reliability Engineering
General Motors. Partner with subject matter experts to define metrics, analytical objectives, thresholds, and decision criteria.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in statistical analysis, machine learning, and anomaly detection to drive data-driven decision-making. Proficient in building analytical pipelines and visualizations that enhance operational workflows and support vehicle and software launches.
Highest-signal resume keywords
Statistical AnalysisMachine LearningPython ProgrammingSQL QueryingData Pipeline Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical AnalysisMachine LearningAnomaly DetectionData AnalysisData TransformationForecastingClassificationData ValidationData Quality AssessmentOperational Dashboard Development
Soft Skills
CommunicationOwnershipSound JudgmentResponsivenessMentoring
Tools & Technologies
AzureDatabricksSQLGitCI/CD
Industry Keywords
Vehicle DataTelematicsDiagnostic DataWarranty DataSoftware-Version DataTime-Series DataEvent DataFleet-Level AnalysisVIN-Level InvestigationSoftware-Quality Monitoring
Tech Stack
Tools & technologiesAzureCloudPythonSQL
About the role
Key responsibilities & impact- Partner with subject matter experts to define metrics, analytical objectives, thresholds, and decision criteria.
- Analyze vehicle, software, warranty, diagnostic, fleet, and product-quality data to identify trends, anomalies, relationships, and emerging risks.
- Design, test, and deploy anomaly-detection and pre-emptive-monitoring algorithms.
- Build end-to-end analytical pipelines that transform raw data into insights, dashboards, alerts, and operational workflows.
- Create customer-focused visualizations for engineering and quality teams to investigate fleet, VIN, and software-version issues.
- Develop alerting solutions for high-priority vehicle and product issues.
- Establish validation approaches using historical data, controlled testing, domain expertise, and real-world observations where appropriate.
- Communicate findings, assumptions, limitations, and recommendations to technical and non-technical audiences.
- Improve model, query, pipeline, and architecture performance, scalability, reliability, and cost efficiency.
- Standardize analytical processes and adopt modern Azure-based technologies.
- Support vehicle and software launches with readiness insights and analytical tools.
- Document analytical methods, data lineage, operating procedures, and known limitations.
- Contribute reusable patterns and best practices and mentor others.
- Deliver critical analytics features, pre-emptive monitors, trusted dashboards, and alerts for engineering, quality, warranty, and launch teams.
Requirements
What you’ll need- Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
- Significant experience applying statistical analysis, machine learning, anomaly detection, forecasting, classification, or related data-science methods to real-world business or engineering problems.
- Strong Python skills for data analysis, algorithm development, automation, and production-oriented data workflows.
- Experience querying, transforming, validating, and analyzing large and complex datasets using SQL and modern data technologies.
- Demonstrated ability to take an analytical solution from problem definition through testing, deployment, monitoring, and continuous improvement.
- Experience communicating analytical results and recommendations to stakeholders with different levels of technical expertise.
- Strong data-quality mindset, including the ability to investigate discrepancies, assess assumptions, and explain limitations.
- Demonstrated ownership, sound judgment, responsiveness, and ability to deliver work to completion in a cross-functional environment.
- GM does not provide immigration-related sponsorship; applicants must not require GM immigration sponsorship now or in the future.
- Preferred: Experience with Databricks, data pipelines, workflow orchestration, or comparable cloud technologies.
- Preferred: Experience building operational dashboards, alerting tools, or decision-support products.
- Preferred: Experience with vehicle, telematics, diagnostic, warranty, and software-version data.
- Preferred: Experience with time-series data, event data, fleet-level analysis, VIN-level investigation, or software-quality monitoring.
- Preferred: Experience with Git-based workflows, CI/CD, or Databricks Asset Bundles.
- Preferred: Familiarity with data modeling, data lineage, reusable analytical patterns, and scalable solution design.
- Preferred: Experience evaluating AI/ML methods for detection quality, false positives, and operational burden.
- Preferred: Experience mentoring peers, sharing technical best practices, or leading work across organizational boundaries.
Benefits
Comp & perks- Paid time off including vacation days, holidays, and supplemental benefits for pregnancy, parental and adoption leave.
- Healthcare, dental and vision benefits including health care spending account and wellness incentive.
- Life insurance plans to cover you and your family.
- Company and matching contributions to a Defined Contribution Pension plan.
- GM Vehicle Purchase Plan for you, your family, and friends.