{"id":2754952,"url":"https://alion.io/job/uber-sr-scientist-tech-science-5","title":"Sr Scientist, Tech - Science","company":{"id":230,"name":"Uber","domain":"uber.com","url":"https://alion.io/company/uber","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":93,"open_postings":137,"ghost_share":0,"stale_share":0.431,"repost_share":0.073,"time_to_fill_p50_days":8,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":132000,"max_usd":250000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1030},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SQL","optional":false},{"name":"Git","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-16T23:18:45Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T13:59:20Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nThe Rider team is the centerpiece of the Uber consumer experience, owning the flagship app used by over 100M monthly active users.\nThe Personalization team builds the ranking, recommendation and agentic systems that shape what millions of riders see and do in the app. We are looking for a Senior Scientist to bring scientific rigor to this work and to help set its direction. You will be an experimentation and causal inference expert with strong ML fluency who will own how we measure whether our AI/ML systems are working, and you will use deep-dive analysis to show the team where the biggest opportunities are. You will partner closely with applied scientists and ML engineers to ensure we can trust our results, understand why a model wins or loses, and focus the roadmap on what moves rider outcomes.\nWhat You'll Do\nOwn experimentation for personalization. Design, run and analyze experiments for ranking, recommendation and agentic features. This includes interleaving, holdouts, switchbacks, and variance reduction (e.g., CUPED/regression adjustment). Use difference-in-differences, synthetic control, instrumental variables, regression discontinuity, or propensity methods, and be clear about the identifying assumptions.\n\nClose the gap between offline and online evaluation. Build offline evaluation and simulation frameworks (e.g., NDCG/MRR, counterfactual and off-policy evaluation). Diagnose why offline gains don't show up online, including novelty effects, distribution shift and training-serving skew.\n\nGo deep on the data. Run deep-dive analyses of rider behavior, funnels, and ranking and recommendation outcomes. Include heterogeneous treatment effects and long-term versus short-term tradeoffs. Surface proactive insights that change what the team builds next.\n\nDefine success, guardrail and long-term metrics for personalization surfaces. Make results easy to find and use through dashboards, query templates and debugging tools.\n\nShape the roadmap. Turn analysis into clear recommendations for PMs, engineers and applied scientists, and help the team decide which bets to prioritize and which to stop.\n\nRaise the scientific bar. Review experiment designs and model evaluations, mentor junior scientists, and share best practices across the team. Partner with applied scientists on experiment design for new ML approaches (e.g., listwise ranking, reinforcement learning, evidential deep learning, LLM-based/agentic applications).\n\nStay current on advances in causal inference, experimentation and personalization AI/ML.\n\nBasic Qualifications\nM.S. or Bachelor's degree in Statistics, Economics, Computer Science, Mathematics, Operations Research, or another quantitative field.\n\n5+ years of industry experience as a Data Scientist, Applied Scientist, Research Scientist, or equivalent, with a track record of owning experimentation and/or causal inference end to end.\n\nDeep expertise in experiment design and analysis (power, variance reduction, multiple testing, interference/network effects) and in observational causal inference methods.\n\nWorking knowledge of machine learning. You can critique and debug models, understand ranking/recommender approaches and common failure modes, and use ML as an analytical tool.\n\nProficiency in Python and SQL, plus PySpark or similar big-data tools. You can build production-quality analysis pipelines.\n\nStrong business and product sense. You can frame ambiguous problems, take them end to end, and communicate recommendations clearly to technical and non-technical partners\n\nPreferred Qualifications\nPh.D. or M.S. in Statistics, Economics, or a related field with a causal inference or econometrics focus.\n\nExperience evaluating ranking, recommender, search or feed systems (offline vs. online evaluation, interleaving, counterfactual evaluation).\n\nExperience with heterogeneous treatment effects, uplift modeling, or long-term effect estimation/surrogate metrics.\n\nExperience measuring or evaluating LLM-based or agentic applications.\n\nHands-on experience with ML frameworks (scikit-learn, PyTorch or TensorFlow), and with good practices such as design documentation, testing and Git.\n\nHistory of mentoring and raising the technical bar for a team.\n\n For New York City, NY-based roles: The base salary range for this role is USD $190,000 per year - USD $211,000 per year.\nYou will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.","description_format":"text","description_chars":4586,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Delivery","Ride Hailing"],"lifecycle":[{"event":"open","at":"2026-10-17T01:27:04Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":8,"reasons":["conf:4","velocity","win:early","comp:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/uber-sr-scientist-tech-science-5","json_url":"https://alion.io/job/uber-sr-scientist-tech-science-5.json","meta":{"generated_at":"2026-10-17T14:44:31Z","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":19853,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"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":230},"rest":"https://alion.io/mcp/rest/get_company?id=230"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fuber-sr-scientist-tech-science-5"},{"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%2Fuber-sr-scientist-tech-science-5"},{"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%2Fuber-sr-scientist-tech-science-5"},{"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/uber-sr-scientist-tech-science-5\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fuber-sr-scientist-tech-science-5"}]}