Data Scientist - Interactive Safety Visualizations and Reporting

General Motors

4d•$129,400 - $198,400•Remote

About The Position

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Safety Assurance for Effective Autonomous Driving Software (SAFE-ADS) department is part of GM’s Global Product Safety, System, and Certification organization. Our mission is to help GM deliver trustworthy automated-driving products. As the central authority for automated-driving system safety, the department brings together experts from across the company to develop and maintain a comprehensive safety case, including safety performance indicators for GM’s automated-driving technologies. GM’s vision is zero crashes, zero emissions, and zero congestion, and autonomous vehicle safety is essential to achieving that vision. The AV Safety Engineering Analytics team within SAFE-ADS provides data and analytics capabilities that support automated-vehicle safety decision-making. The team integrates continuously flowing data from vehicle systems, company databases, third-party services, federal agencies, and state departments of transportation to inform system design, quantify driving performance, support continuous uptime analyses, and investigate specific safety questions. This Data Scientist will lead the development of interactive safety visualizations and automated reporting that facilitate interpretation by stakeholders at all levels of the organization, leading to and rapid decision making and continuous automated driving system improvement. This role will own the data products and visualization experiences that enable users to drill-down from large-scale aggregate trends to filtered subsets, individual records, and supporting data. The successful candidate will partner with safety engineers, analysts, data engineers, program teams, and leaders to translate complex safety information into clear, performant, and trusted communication tools. The role spans the full analytics lifecycle, from stakeholder discovery and data modeling to user-experience design, statistical interpretation, and production delivery of continuous up-time interactive visualizations.

Requirements

Bachelor’s degree in data science, statistics, mathematics, computer science, engineering, or a related field.
5+ years of relevant experience in data science, analytics engineering, data visualization, reporting, or a related discipline.
Strong programming experience with Python and SQL, with experience in python based applications
Experience developing complex, interactive, Python-based visualizations or analytical applications using contemporary visualization libraries, for large-scale datasets and user bases.
Experience designing data models, analytical tables, data marts, or data pipelines that support reporting and visualization.
Experience supporting hourly or intra-day data refreshes, application performance, and service-level objectives.
Experience translating ambiguous stakeholder needs into clear analytical requirements and usable data products.
Practical understanding of uncertainty, statistical comparison, statistical inference, and statistical testing.
Strong problem-solving mindset and a proactive attitude toward learning and self-improvement.

Nice To Haves

Experience with dashboard UI/UX design
Experience implementing automated data-quality and completeness checks, metric validation, and/or data lineage.
Experience working with automotive, transportation, mobility, fleet, robotics, or other operationally complex, time series datasets.
Experience analyzing safety, reliability, availability, performance, human factors, or risk-related metrics.
Experience with software development practices such as version control, automated testing, code review, continuous integration, and deployment.
Experience designing dashboards for different audiences, including technical practitioners and executive decision-makers.
Ability to build trusted relationships with technical stakeholders and influence decisions without direct authority.

Responsibilities

Design and develop interactive safety visualizations that allow users to move from aggregate performance trends to filtered subsets, individual records, and supporting data points.
Build and maintain dashboards and analytical applications that provide clear, intuitive experiences for technical users and leadership audiences.
Develop data marts, analytical tables, and other data products that support scalable visualization and reporting.
Define, implement, and curate business logic for metric reporting aggregations, uptime measures, benchmarks, comparisons, and supporting dimensions.
Develop data-quality checks, assertions, and monitoring processes that improve trust in published metrics and identify issues before they affect reporting.
Optimize data preparation, modeling, and application workflows to support hourly or intra-day refresh expectations and reliable service-level performance.
Automate reporting that presents metric definitions, data sources, data lineage, quality status, statistical context, and continuous-improvement progression.
Apply practical statistical methods to visually characterize uncertainty, compare performance, interpret variation, and conduct appropriate statistical tests.
Partner with safety engineers and technical stakeholders to understand analytical needs, clarify requirements, and deliver fit-for-purpose tools.
Communicate complex analytical results in clear visual and written formats appropriate for both technical stakeholders and leaders.
Establish reusable visualization, data-modeling, testing, and documentation patterns that improve consistency and reduce recurring manual effort.