Senior Data Scientist Vehicle Reliability Engineering

Job Description

Vacancy Status:

Yes - This posting is for an existing vacancy within the organization and is open to new applications. (Backfill)

AI Disclosure:

As part of the application process Artificial Intelligence will be used in the hiring process for this role.

Hybrid - This role is categorized as hybrid. This means the successful candidate is expected to report to Markham three times per week at minimum or other frequency dictated by the business.

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.

• 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 andwhen appropriatereal-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.

• 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 charging 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 definitions validation methods data quality checks and repeatable operating practices.

• Bachelors 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.

• Experience with Databricks data pipelines workflow orchestration or comparable cloud technologies.

• Experience building operational dashboards alerting tools or decision-support products for engineering quality warranty manufacturing fleet or customer-facing teams.

• Experience working with vehicle telematics diagnostic warranty software-version.

• Experience with time-series data event data fleet-level analysis VIN-level investigation or software-quality monitoring.

• Experience with modern deployment practices such as Git-based workflows CI/CD or Databricks Asset Bundles.

• Familiarity with data modeling data lineage reusable analytical patterns and scalable solution design.

• Experience evaluating whether AI/ML methods can improve detection quality while controlling false positives and operational burden.

• Experience mentoring peers sharing technical best practices or leading work across organizational boundaries.

• Selects appropriate analytical methods based on the business question data limitations and decision context.

• Tests assumptions and validates results using sound statistical and domain-informed practices.

• Balances detection sensitivity false positives explainability and operational usefulness.

• Starts with how internal customers make decisions and designs the solution around that workflow.

• Converts complex data into clear actionable insights.

• Builds trust through responsiveness follow-through transparency and data quality.

• Owns outcomes across discovery development deployment support and improvement.

• Troubleshoots issues quickly and follows assigned work through completion.

• Designs solutions that are maintainable scalable secure and cost-conscious.

• Explores practical new methods and technologies when existing approaches do not adequately solve the problem.

• Identifies opportunities to automate manual processes and improve performance.

• Takes educated risks while using validation and safeguards to minimize downside.

• Works effectively with engineering quality warranty analytics and business partners.

• Listens actively shares information and builds productive relationships.

• Mentors others and contributes reusable knowledge to the organization.

• Develop pre-emptive detection for emerging product-quality issues and other customer-impacting patterns.

• Build algorithms that summarize vehicle behavior and help engineering teams identify problematic conditions.

• Create dashboards and alerts that provide timely visibility into vehicle concerns launch readiness fleet behavior and production risks.

• Improve ingestion query and dashboard performance while reducing manual preparation and cloud-resource costs.

• Integrate additional signalsincluding warranty diagnostic software and operational datato improve detection confidence and business context.

Success will be evaluated through a combination of technical quality customer impact and organizational contribution including:

• Accuracy precision recall timeliness and operational usefulness of deployed detection solutions.

• Adoption and satisfaction among engineering quality warranty and launch-readiness customers.

• Reduction in time required to investigate issues prepare meetings extract data or respond to high-priority risks.

• Reliability performance scalability maintainability and cost efficiency of production analytics products.

• Completion of committed critical features and effective production support.

• Quality of documentation data definitions validation practices and knowledge sharing.

• Demonstration of GM behaviors: Own the Outcome Think Customer Innovate Now Win with Integrity Commit to Customers and One Team.

Compensation:

The salary range for this role is $115000 to $ 164600. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.

GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP NOW OR IN THE FUTURE

Benefits:

The goal of the General Motors of Canada total rewards program is to support the health and well-being of you and your family. Our comprehensive compensation plan currently includes the following benefits in addition to many others:

• 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 to help you save for retirement.
• GM Vehicle Purchase Plan for you your family and friends.

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