Data Scientist, ADAS Analytics Machine Learning

At Mercedes-Benz Research & Development North America (MBRDNA), we are committed to delivering world-class automotive technologies that push the boundaries of what is possible. Our teams of highly skilled engineers and designers use cutting-edge software and technology, to enhance the driving experience and reduce environmental impact.

Position Overview

As Mercedes-Benz scales ADAS across production fleets, the US ADAS Data & Forensics team is building applied-AI capabilities that go beyond descriptive KPI reporting. These capabilities include vision-language models that analyze SSR Scene Safety Recording footage, LLM-based event classification and reasoning, embedding-based semantic retrieval over driving scenarios, automated scenario discovery, natural-language event descriptions, and model-based ranking that surfaces the most important events from thousands of daily drives. As a Data Scientist, you will develop and deploy these capabilities in production for two stakeholder groups\: management, through fleet-performance summaries, trend forecasts, and AI-generated event narratives; and engineers, through granular model-assisted analysis of ADAS calibration, scenarios, edge cases, and behavioral patterns across road types, weather, firmware, and driver cohorts.

Relocation assistance (domestic or international) is not provided for this position.​Job Responsibilities\:

• Apply vision-language models to SSR video and combine video analysis with structured telemetry to create multimodal event representations.
• Build LLM classification and reasoning pipelines that triage events by severity and root cause and generate human-readable summaries of takeovers, safety events, and deactivations.
• Design embedding pipelines and semantic search for similar-event retrieval, and develop unsupervised clustering methods that discover recurring scenarios and edge-case families at fleet scale.

• Build model-based ranking and scoring systems that reduce manual event triage, develop active-learning loops using engineer feedback, and proactively detect fleet-level anomalies.

• Quantify the impact of firmware updates and configuration changes on KPIs, segment driver cohorts, and design A/B and quasi-experimental frameworks.

• Analyze campaign effectiveness, build coverage-optimization models, and develop automated fleet-quality scoring.
• Deploy models into PySpark and Delta Lake pipelines and the FastAPI analytics API, build evaluation frameworks for foundation-model outputs, and operate on the Azure data platform, including ADLS, Synapse, and Container Apps.