Data Scientist, ADAS Analytics Machine Learning
San Jose, CA, United StatesYesterday
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Key details
- Location
- San Jose, CA, United States
- Work style
- Onsite
- Level
- Mid level
- Experience
- 3–6 yrs
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- Yesterday
Skills
About the role
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.
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.
Minimum Qualifications:
- Bachelor's or Master's degree in Data Science, Machine Learning, Statistics, Computer Science, or a related quantitative field. A Master's degree or PhD is beneficial but not required; demonstrated experience carries equal weight.
- 3+ years of professional experience.
- 2-5 years of experience in data science or applied machine learning.
- Depth in at least one of the following: applied foundation models such as LLMs, VLMs, or embeddings; classical machine learning in production; or statistical experimentation.
- Strong Python skills using NumPy, pandas, and scikit-learn, with an emphasis on clean, testable code, and strong SQL skills.
- Experience with ML model development, including feature engineering, model selection, and evaluation on real data.
- Solid statistics knowledge, including hypothesis testing, regression, and experimental design.
- Ability to communicate findings clearly to engineers and management through reports, presentations, and dashboards.
- Ability to turn ambiguous questions into structured analytical approaches.
Strongly Preferred:
- LLM or VLM application experience, including prompt engineering, structured outputs, and evaluation.
- Embedding models and vector similarity for retrieval or clustering.
- PySpark for large-scale processing; candidates with strong pandas experience may ramp up.
- Time-series analysis or anomaly detection.
Nice to Have:
- Multimodal foundation models applied to video or image data.