Machine Learning Engineer, Simulation

Required

• A Bachelor’s degree in Data Science, Computer Science, Engineering, Economics, Statistics, or a related scientific or analytic field.
• Minimum 5 years of experience in data analytics, safety or operational performance measurement, or business/technical intelligence within complex engineering, financial, transportation, or safety-critical organizations.
• Demonstrated expertise working with both quantitative and qualitative data and metrics, including developing metrics based on industry best practices or standards, evaluating and maintaining metric quality, and advising stakeholders on metric interpretation and limitations.
• Proficiency with modern data analytics, visualization, and reporting tools (e.g., SQL, Python/R, BI dashboards, and data pipelines) to collect, validate, and communicate performance data to executive leadership and governance committees.
• Proven ability to pull together and synthesize disparate metrics to drive continual improvement and verify the effectiveness of corrective actions.
• Strong cross-functional collaboration and communication skills, with experience translating complex technical and operational signals into clear, actionable narratives for senior decision-makers without direct authority.
• An advanced degree (Master’s or PhD) in Data Science, Computer Science, Engineering, Economics, Statistics, or a related scientific or analytic discipline.
• Hands-on familiarity with automotive, transportation, or aviation Safety Management System frameworks and safety performance indicators.
• Direct experience interpreting and implementing safety performance measurement requirements from vehicle, transportation, or aviation safety regulations and standards.
• Experience leveraging Artificial Intelligence (AI) / Large Language Model (LLM) workflows or automated data pipelines to synthesize qualitative and quantitative safety signals, detect emerging trends, and streamline executive reporting.
• Experience designing and running closed-loop performance governance processes, including threshold alerting, root-cause and CAPA tracking, and statistical or empirical verification of mitigation effectiveness.
• Experience partnering with software/data engineering teams to define data schemas and business requirements for scalable internal analytics and reporting platforms.