Data Scientist ADAS Analytics Machine Learning

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.

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.
• 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.

Preferred Qualifications:

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.

• RAG or vector-database systems such as FAISS, pgvector, or Pinecone.

• Spatial or geospatial clustering with DBSCAN or HDBSCAN.

• Ranking or recommendation systems, active learning, PyTorch, or TensorFlow.

• Delta Lake or Parquet; FastAPI or model-serving APIs; MLOps platforms such as MLflow or Weights & Biases.

• Cloud-platform experience in Azure, AWS, or GCP.

• Vehicle telemetry or automotive-domain experience; campaign analytics or A/B testing at scale.

• Data privacy, including CCPA or GDPR, for vehicle data and foundation models.

• English required; German is an advantage.