Data Scientist, ADAS Analytics Machine Learning

Mercedes-Benz Research & Development North America, Inc.

San Jose (CA)

On-site

USD 118,000 - 148,000

Full time

16 hours ago

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Benefits offered by this job

Medical, dental, vision insurance

401(k) with employer match

Up to 15 company-paid holidays

Paid time off and parental leave

Tuition assistance

Job summary

Mercedes-Benz Research & Development North America, Inc. is seeking a data scientist to scale ADAS capabilities using vision-language models, embeddings, and LLM-based classification.

You will build end-to-end pipelines, deploy models in production, and collaborate with engineers and management on fleet-scale analyses. The role emphasizes translating ambiguous questions into structured analytics, operating on large datasets, and delivering actionable insights with robust validation and

Qualifications

Bachelor's or Master's in Data Science, ML, Statistics, Computer Science or related field
3+ years of professional experience
2-5 years in data science or applied machine learning
Depth in foundation models, VLMs, or embeddings or traditional ML in production
Strong Python with NumPy, pandas, scikit-learn and strong SQL
Experience with ML model development including feature engineering and evaluation
Strong statistics knowledge: hypothesis testing, regression, experiments
Ability to communicate findings to engineers and management
Ability to turn ambiguous questions into structured analytical approaches

Responsibilities

Apply vision-language models to SSR video and combine with telemetry to create multimodal event representations
Build LLM classification and reasoning pipelines; triage events by severity and provide summaries
Design embedding pipelines and semantic search for similar-event retrieval and clustering
Develop model-based ranking and active-learning loops; detect fleet-level anomalies
Quantify impact of firmware updates on KPIs; segment driver cohorts and run AB/quasi experiments
Analyze campaign effectiveness and develop fleet-quality scoring
Deploy models into PySpark/Delta Lake pipelines and FastAPI analytics API; operate on Azure data platform

Skills

Foundation models (LLMs/VLMs/embeds)

Python (NumPy, pandas, scikit-learn)

SQL

Statistics & experimental design

Communication to engineers/management

Structured analytics approach

Machine learning model development

Change detection & anomaly analysis

Time-series/forecasting basics

LLM/VLM application experience

Education

Bachelor's or Master's in Data Science, ML, Statistics, CS

PhD beneficial but not required

Tools

PySpark

Delta Lake

FastAPI

MLflow

Weights & Biases

Azure

AWS/GCP

Job description

Aufgaben

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.

Qualifikationen

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.
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.

PRIVACY NOTICE FOR CALIFORNIA RESIDENTS

https://mbrdna.com/california-employee-privacy-notice/

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