Senior Data Scientist - Vehicle Reliability Engineering

Overview

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] .

Position Overview

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.

Key Responsibilities

• 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.
• 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.
• 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 technica

Expected Business Outcomes

Required Qualifications