Jobs / United States / General Motors Company
Senior Data Engineer - Vehicle Quality Engineering
General Motors Company · 🇺🇸 2 Locations
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What General Motors Company paid sponsored hires in similar roles22 certified filings for “Senior Software Engineer” (Software Quality Assurance Analysts and Testers) in MI: $112k–$137k, median $125k. Most were filed at wage level IV (68%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
Or apply yourself on the official page →
Sponsor Radar — General Motors Company
The US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for General Motors Company →
About the role
Job Description This role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. The Role The Senior Data Scientist will develop and operationalize data science solutions that identify emerging vehicle, software, and product-quality issues before they become larger customer or launch risks. This role partners closely with Product Quality, Warranty, Vehicle Engineering, software teams, and other subject matter experts to translate complex operational questions into trustworthy metrics, detection algorithms, dashboards, and alerting workflows. The successful candidate will combine strong statistical and analytical judgment with the ability to build production-ready data products. They will work across the full lifecycle: understanding the customer problem, validating the data, developing and testing an analytical approach, deploying the solution, monitoring its performance, and continuously improving adoption and scalability. What You’ll Do • Partner with subject matter experts to define meaningful metrics, analytical objectives, thresholds, and decision criteria. • Conduct comprehensive, unbiased analysis to identify trends, anomalies, relationships, and emerging risks in vehicle, software, warranty, diagnostic, fleet, and product-quality data. • Design, test, and deploy anomaly-detection and pre-emptive-monitoring algorithms for customer-impacting issues. • Build end-to-end analytical pipelines that transform raw data into reliable insights, dashboards, alerts, and operational workflows. • Create customer-focused visualizations that allow engineering and quality teams to investigate fleet, VIN, and software-version, • Develop alerting solutions that help internal customers respond quickly to high-priority vehicle and product issues. • Establish validation approaches using historical data, controlled testing, domain expertise, and—when appropriate—real-world observations. • Communicate findings, assumptions, limitations, and recommendations clearly to technical and non-technical audiences. • Improve solution performance, scalability, reliability, and cost efficiency through model, query, pipeline, and architecture improvements. • Reduce complexity and redundant data handling by standardizing analytical processes and adopting modern Azure-based technologies. • Support vehicle and software launches by delivering readiness insights and analytical tools. • Document analytical methods, data lineage, operating procedures, and known limitations so solutions can be maintained and adopted across teams. • Contribute reusable patterns, best practices, and mentoring that increase the impact of the broader analytics organization. Expected Business Outcomes • Deliver committed critical analytics features for internal customers with clear acceptance criteria and production support plans. • Launch or improve pre-emptive monitors that identify emerging vehicle, software, or product-quality concerns earlier than traditional reactive processes. • Provide trusted dashboards and alerts that enable real-time, data-driven decisions for engineering, quality, warranty, and launch teams. • Improve the performance, scalability, usability, and cost profile of existing analytics products. • Demonstrate measurable customer value through reduced manual effort, faster issue investigation, improved launch readiness, or earlier risk mitigation. • Increase adoption by working directly with software domains and operational teams to understand how insights are used and where capabilities should expand. • Strengthen analytical governance through documented d