Staff Data Scientist (Fraud, Risk)

About the Team

Uber is looking for an experienced and motivated Scientist to join ourGlobal Safety &RiskTeam. In this role, you will significantly contribute to the physical safety and security of millions of Uber users globally. You will apply your knowledge in data analysis, machine learning, andstatisticalmodelingto generateinsightsand develop new algorithms that identify and prevent safety incidents before they occur.

This position offers the chance to innovate our technology stack by utilizing the latest breakthroughs inPredictiveModeling,CausalInference, andReal-timeRiskSystemsto create intricate autonomous safety frameworks throughout the Uber ecosystem.

About the Role

- Conduct thorough analyses of large,imbalanced datasetsto identify trends, patterns, and opportunities for improving safety incident detection.
- Design, implement, and optimizebinaryclassificationmodelsand algorithms to predict the probability of high-severity incidents.
- Generate actionableinsightsfromriskdata and communicate findings to stakeholders, balancing safety interventions with marketplace growth.
- Work as athought expertfor your cross-functional partners (Product, Ops, and Engineering), pushing the boundaries of how Uber defines and mitigatesrisk.
- Designcomplexexperiments(Diff-in-Diff,SyntheticControls)and interpret results to draw impactful conclusions in a marketplace environment where classicalA/Btestingis often not feasible.
- Define how your cross-functional team measures success by developingSafety &Riskmetrics(e.g., Recall, Precision-Recall AUC, Probability Calibration) in partnership with global stakeholders.
- Stay current with the latest advancements inSupervised Learning,CausalInference, and Anomaly Detection.

Technical Skills

Required

- Seniorand/orStaffseniorityworking as a Data Scientist,AppliedScientist, or Machine Learning Engineer.
- Experience building and deployingBinaryClassification systemsin production for large-scale, high-stakes applications (e.g.,Fraud, Safety, orRisk).
- Deepexperience inExperimental Designbeyond classicalA/Btesting, includingQuasi-experiments(Diff-in-Diff,SyntheticControl) orMarketplaceexperiments(Switchbacks).
- Expertise in handlingExtreme Class Imbalanceand optimizingmodelsfor rare event detection.
- Experience usingPythonandSQLto work with massive, high-dimensional data sets at scale.
- Solid foundation inStatisticalMethodologiessuch as probability calibration, sampling, andhypothesistesting.

Preferred

- Experience withGeospatial data analysis(e.g., H3, S2 geometry).
- Background inReal-timeInferencesystems (working with streaming data like Kafka or Flink).
- Knowledge ofCausalInferenceto measure the incremental impact of safety interventions.

Please note: this hybrid position is based in São Paulo or Rio de Janeiro, Brazil - welcoming both local professionals and those open to relocating to those cities.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let's move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.