Data Scientist ADAS Analytics Machine Learning

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.

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.

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.

The successful candidate’s starting pay will be determined based on a wide range of factors, including, but not limited to, job-related education, skills, and experience, geographic location, and market conditions. The current salary range for this position is as follows and may be modified in the future: $118,200 - $147,700.

This position may involve access to export-controlled technology, hardware, or software under the Export Administration Regulations (EAR), 15 C.F.R. Parts 730-774 and possibly technical data, defense articles and defense services subject to the International Traffic in Arms Regulations (ITAR), 22 C.F.R. Parts 120-130. As such, Mercedes-Benz Research & Development North America may be required to obtain an export license or authorization in accordance with United States law.

Why should you apply?

Here at MBRDNA, you create digital ecosystems around cars, you design a language between humans and machines, you make a car even more intelligent - you make the new reality for cars. MBRDNA was honored as one of the "Best Places to Work" by BuiltIn in January 2024, a testament to our commitment to creating an exceptional work environment. At each of our offices, we foster a culture of collaboration and continuous learning, ensuring every team member can thrive and innovate.

Benefits for Full-Time * Employees Include:

• Medical, dental, and vision insurance for employees and their families

• 401(k) with employer match

• Up to 15 company-paid holidays

• Paid time off (flexible time off for salaried employees), sick time, and parental leave

• Tuition assistance program

• Wellness/Fitness reimbursement programs

Internships & Contractors excluded from Full-Time Employee benefits

MBRDNA is an equal opportunity employer (EOE) and strongly supports diversity in the workforce. MBRDNA only accepts resumes from approved agencies who have a valid Agency Agreement on file. Please do not forward resumes to our applicant tracking system, MBRDNA employees, or send to any MBRDNA location. MBRDNA is not responsible for any fees or claims related to receipt of unsolicited resumes.

Mercedes-Benz Research and Development North America, Inc.

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