{"id":3509965,"url":"https://alion.io/job/mercedes-benz-data-scientist-adas-analytics-machine-learning","title":"Data Scientist, ADAS Analytics Machine Learning","company":{"id":136,"name":"Mercedes-Benz","domain":"mercedes-benz.com","url":"https://alion.io/company/mercedes-benz","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"A","score":97,"open_postings":54,"ghost_share":0.019,"stale_share":0.074,"repost_share":0.019,"time_to_fill_p50_days":29,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":118200,"max":147700,"currency":"USD","period":"year","gross":null,"usd_annual":147700},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NumPy","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Time Series","optional":false},{"name":"VLM","optional":false},{"name":"A/B Testing","optional":true},{"name":"AWS","optional":true},{"name":"FAISS","optional":true},{"name":"GCP","optional":true},{"name":"GDPR","optional":true},{"name":"MLFlow","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-10-29T00:00:00Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-01T03:10:45Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Aufgaben\n 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.\nPosition Overview\nAs 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.\nRelocation assistance (domestic or international) is not provided for this position.\nJob Responsibilities:\nApply vision-language models to SSR video and combine video analysis with structured telemetry to create multimodal event representations.\nBuild LLM classification and reasoning pipelines that triage events by severity and root cause and generate human-readable summaries of takeovers, safety events, and deactivations.\nDesign 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. \nBuild model-based ranking and scoring systems that reduce manual event triage, develop active-learning loops using engineer feedback, and proactively detect fleet-level anomalies. \nQuantify the impact of firmware updates and configuration changes on KPIs, segment driver cohorts, and design A/B and quasi-experimental frameworks. \nAnalyze campaign effectiveness, build coverage-optimization models, and develop automated fleet-quality scoring. \nDeploy 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. \n Qualifikationen\n Minimum Qualifications:\nBachelor'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. \n3+ years of professional experience. \n2-5 years of experience in data science or applied machine learning. \nDepth in at least one of the following: applied foundation models such as LLMs, VLMs, or embeddings; classical machine learning in production; or statistical experimentation. \nStrong Python skills using NumPy, pandas, and scikit-learn, with an emphasis on clean, testable code, and strong SQL skills. \nExperience with ML model development, including feature engineering, model selection, and evaluation on real data. \nSolid statistics knowledge, including hypothesis testing, regression, and experimental design. \nAbility to communicate findings clearly to engineers and management through reports, presentations, and dashboards. \nAbility to turn ambiguous questions into structured analytical approaches. \nStrongly Preferred:\nLLM or VLM application experience, including prompt engineering, structured outputs, and evaluation. \nEmbedding models and vector similarity for retrieval or clustering. \nPySpark for large-scale processing; candidates with strong pandas experience may ramp up. \nTime-series analysis or anomaly detection. \nNice to Have:\nMultimodal foundation models applied to video or image data. \nRAG or vector-database systems such as FAISS, pgvector, or Pinecone. \nSpatial or geospatial clustering with DBSCAN or HDBSCAN. \nRanking or recommendation systems, active learning, PyTorch, or TensorFlow. \nDelta Lake or Parquet; FastAPI or model-serving APIs; MLOps platforms such as MLflow or Weights & Biases. \nCloud-platform experience in Azure, AWS, or GCP. \nVehicle telemetry or automotive-domain experience; campaign analytics or A/B testing at scale. \nData privacy, including CCPA or GDPR, for vehicle data and foundation models. \nEnglish required; German is an advantage. \nThe 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.\nThis 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.\nWhy should you apply?\nHere 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.\nBenefits for Full-Time* Employees Include:\nMedical, dental, and vision insurance for employees and their families\n401(k) with employer match\nUp to 15 company-paid holidays\nPaid time off (flexible time off for salaried employees), sick time, and parental leave\nTuition assistance program\nWellness/Fitness reimbursement programs\n* Internships & Contractors excluded from Full-Time Employee benefits\nMBRDNA 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.\nMercedes-Benz Research and Development North America, Inc.\nPRIVACY NOTICE FOR CALIFORNIA RESIDENTS\nhttps://mbrdna.com/california-employee-privacy-notice/","description_format":"text","description_chars":6912,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":true,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":["Continuous learning","Flexible time off","Parental leave","Relocation assistance","Vision insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":["Sports Organizations","Automotive Manufacturing","Exhibitions"],"lifecycle":[{"event":"open","at":"2026-10-30T07:20:30Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 12 · green card filings: 2","filings_12m":12,"filings_prev_12m":5,"green_card_filings_12m":2,"median_offered_wage_usd":126071,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":2}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":29,"reasons":["conf:0","velocity","win:early","comp:brand"],"computed_at":"2026-10-31T05:58:55Z"},"pay":{"stated_usd_annual":147700,"is_top_pay":true},"html_url":"https://alion.io/job/mercedes-benz-data-scientist-adas-analytics-machine-learning","json_url":"https://alion.io/job/mercedes-benz-data-scientist-adas-analytics-machine-learning.json","meta":{"generated_at":"2026-11-01T03:32:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":5490,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-02T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":136},"rest":"https://alion.io/mcp/rest/get_company?id=136"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmercedes-benz-data-scientist-adas-analytics-machine-learning"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmercedes-benz-data-scientist-adas-analytics-machine-learning"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmercedes-benz-data-scientist-adas-analytics-machine-learning"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/mercedes-benz-data-scientist-adas-analytics-machine-learning\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmercedes-benz-data-scientist-adas-analytics-machine-learning"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3509965}},{"tool":"search_jobs","arguments":{"company":"mercedes-benz.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3509965},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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