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# Data Scientist - Metrics

- **Company:** May Mobility
- **Location:** Ann Arbor, United States
- **Experience:** Expert
- **Salary:** $163,477.0 - $240,408.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Python (Programming Language), Kinematics, Machine Learning, Motion Planning, NumPy, Robotic Automation Software, SQL Databases, Multi-Agent Systems, Apache Spark, Pandas, Information Technology
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-data-scientist-metrics-may-mobility-7539780

## About the Role

Success in this role typically requires the following competencies:

 * Strong proficiency in Python, SQL, and data analysis tools (e.g., Pandas, NumPy, Spark).
 * Strong understanding of vehicle dynamics, kinematics, agent interactions, and road/traffic elements.
 * Expertise in analyzing high-dimensional or time-series data from sensors, logs, and simulation systems.
 * Excellent technical communication skills with the ability to clearly present complex model designs and results to both technical and non-technical stakeholders.
 * Detail-oriented with a focus on validation, testing, and error detection., * B.S, M.S. or Ph.D. Degree in Engineering, Data Science, Computer Science, Math, or a related quantitative field.
 * 5+ years of experience in data science, applied machine learning, robotics, or autonomous systems.
 * 2+ years working in AV, ADAS, robotics, or another safety-critical domain involving vehicle behavior analysis.
 * Demonstrated experience developing or evaluating safety and/or comfort metrics for autonomous or robotic systems.
 * Hands-on experience working with real-world driving logs and/or simulation data.

Desired

 * Background in motion planning, behavior prediction, or multi-agent interaction modeling.
 * Experience designing metric-driven development, KPIs, and automated triaging pipelines.

## Description

* Develop and refine safety and comfort metrics for evaluating autonomous vehicle performance across real-world and simulation data.
 * Build ML and non-ML models to detect unsafe, uncomfortable, or anomalous behaviors.
 * Analyze large-scale drive logs and simulation datasets to identify patterns, regressions, and system gaps.
 * Collaborate with perception, prediction, behavior, and simulation teams to integrate metrics into workflows.
 * Communicate insights and recommendations to engineering leaders and cross-functional teams.


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