Uber
Sr. Technical Recruiter | ex-Amazon, Meta, VMware, Zillow
About the Role The
Road Safety
team is dedicated to safeguarding the Uber platform by applying cutting-edge data science and machine learning to proactively mitigate and make
rare safety events even rarer . As a
Staff Applied Scientist , you will be responsible for
setting the technical direction
to develop and deploy high-impact, production-ready machine learning models, conducting rigorous deep-dive analyses to inform strategy, and designing/evaluating complex experiments (A/B testing). Your work will play an influential and
highly visible role
in driving critical
product, policy, and engineering decisions
that ensure our platform is as safe as possible for all users globally. What the Candidate Will Do Technical Leadership & Strategy:
Define the strategic roadmap and set the technical direction for developing and deploying large-scale, high-performance machine learning systems focused on proactive safety prediction and mitigation. Modeling & Production:
Design, develop, and deliver sophisticated applied ML models from ideation to production, ensuring robustness and measurable safety impact. Deep-Dive & Insights:
Conduct complex, rigorous deep-dive analyses and causal inference to uncover root causes and identify high-leverage safety opportunities. Experimentation:
Own the design, analysis, and interpretation of A/B experiments to rigorously evaluate product and policy changes before platform rollout. Cross-Functional Influence:
Partner closely with Product Managers, Engineers, and Policy teams to translate data-driven insights into critical product features and company-wide safety policies. Basic Qualifications Education:
Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field, OR equivalent experience. Experience:
8+ years (with Ph.D.) or 8+ years (with M.S. or B.S.) of industry experience building and deploying machine learning models or conducting high-impact applied data science in a large-scale production environment. Technical Depth:
Expert proficiency in core machine learning principles, including classification, regression, time series analysis, and causal inference. Programming:
High proficiency in at least one programming language (e.g.,
Python
or Scala) and expertise in data manipulation using
SQL . System Scale:
Demonstrated experience designing and delivering end-to-end ML solutions that operate at significant scale (handling large datasets and high-velocity systems). Strategic Impact:
Proven track record of influencing product or policy decisions using rigorous analysis, experimentation (A/B testing), and clear communication of complex technical results to non-technical stakeholders. Preferred Qualifications Insurance/Actuarial Fundamentals:
Applied knowledge of core insurance concepts such as: Risk Modeling:
Understanding of concepts like frequency, severity, and loss development. Loss Cost & Pricing:
Familiarity with how safety events translate into financial loss (expected claims/payouts) and the inputs for risk-based pricing or economic valuation of safety interventions. Telematics:
Experience leveraging granular sensor or telematics data to model driver behavior and assess accident probability. Seniority level
Director Employment type
Full-time Job function
Information Technology Industries: Internet Marketplace Platforms, Technology, Information and Media, and Data Infrastructure and Analytics Benefits
Medical insurance Vision insurance 401(k) Paid maternity leave Paid paternity leave Location: San Jose, CA Salary: $121,700 - $228,600
#J-18808-Ljbffr
About the Role The
Road Safety
team is dedicated to safeguarding the Uber platform by applying cutting-edge data science and machine learning to proactively mitigate and make
rare safety events even rarer . As a
Staff Applied Scientist , you will be responsible for
setting the technical direction
to develop and deploy high-impact, production-ready machine learning models, conducting rigorous deep-dive analyses to inform strategy, and designing/evaluating complex experiments (A/B testing). Your work will play an influential and
highly visible role
in driving critical
product, policy, and engineering decisions
that ensure our platform is as safe as possible for all users globally. What the Candidate Will Do Technical Leadership & Strategy:
Define the strategic roadmap and set the technical direction for developing and deploying large-scale, high-performance machine learning systems focused on proactive safety prediction and mitigation. Modeling & Production:
Design, develop, and deliver sophisticated applied ML models from ideation to production, ensuring robustness and measurable safety impact. Deep-Dive & Insights:
Conduct complex, rigorous deep-dive analyses and causal inference to uncover root causes and identify high-leverage safety opportunities. Experimentation:
Own the design, analysis, and interpretation of A/B experiments to rigorously evaluate product and policy changes before platform rollout. Cross-Functional Influence:
Partner closely with Product Managers, Engineers, and Policy teams to translate data-driven insights into critical product features and company-wide safety policies. Basic Qualifications Education:
Ph.D. in Computer Science, Statistics, Mathematics, Operations Research, or a related quantitative field, OR equivalent experience. Experience:
8+ years (with Ph.D.) or 8+ years (with M.S. or B.S.) of industry experience building and deploying machine learning models or conducting high-impact applied data science in a large-scale production environment. Technical Depth:
Expert proficiency in core machine learning principles, including classification, regression, time series analysis, and causal inference. Programming:
High proficiency in at least one programming language (e.g.,
Python
or Scala) and expertise in data manipulation using
SQL . System Scale:
Demonstrated experience designing and delivering end-to-end ML solutions that operate at significant scale (handling large datasets and high-velocity systems). Strategic Impact:
Proven track record of influencing product or policy decisions using rigorous analysis, experimentation (A/B testing), and clear communication of complex technical results to non-technical stakeholders. Preferred Qualifications Insurance/Actuarial Fundamentals:
Applied knowledge of core insurance concepts such as: Risk Modeling:
Understanding of concepts like frequency, severity, and loss development. Loss Cost & Pricing:
Familiarity with how safety events translate into financial loss (expected claims/payouts) and the inputs for risk-based pricing or economic valuation of safety interventions. Telematics:
Experience leveraging granular sensor or telematics data to model driver behavior and assess accident probability. Seniority level
Director Employment type
Full-time Job function
Information Technology Industries: Internet Marketplace Platforms, Technology, Information and Media, and Data Infrastructure and Analytics Benefits
Medical insurance Vision insurance 401(k) Paid maternity leave Paid paternity leave Location: San Jose, CA Salary: $121,700 - $228,600
#J-18808-Ljbffr