About the role and team

Science at Uber means transforming the complex reality of global data into the insights that power our most significant decisions. As a Senior Data Scientist, you will turn complexity into clarity by deep-diving into massive, multi-dimensional datasets to shape the direction of our core platforms and global marketplaces. This is an analytics-focused role where you will refine ambiguous questions and generate new hypotheses to identify opportunities that improve the user journey for millions of riders, earners, and merchants worldwide.

You will join a high-stakes environment where the work is fast-moving, and the results are felt in the real world every single day. Collaborating closely with Product, Engineering, and Operations, you will serve as a data thought leader, ensuring that strategic decisions are rooted in rigorous insights and experimental results. This role is about influence and operational impact — if you are looking to build and productionize advanced ML models, this is not the team; if you want to navigate the messy reality of global business challenges through data, this is where you’ll grow.

Rate of Pay: $193,232 to $231,879 per Year

You will be eligible to participate in Uber's bonus program, and may be offered other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.

What you’ll do

• Manage and lead a team of data scientists, setting priorities, allocating resources, and overseeing delivery of fraud prevention initiatives in collaboration with cross-functional stakeholders.
• Establish team objectives aligned with business strategy and ensure accountability for performance and results.
• Conduct performance evaluations, set development goals, provide coaching, and make recommendations regarding hiring, promotions, and compensation.
• Responsible for recruitment and workforce planning, defining role requirements, interviewing candidates, and making hiring decisions.
• Design experiments and analyze results to draw actionable conclusions, performing time-series analyses, hypothesis testing, and causal inference to assess impact and identify trends.
• Build predictive models to understand user behavior and forecast performance, as well as create dashboards and reports tailored to various audiences to monitor and communicate key metrics.
• Drive improvements in key performance metrics such as fraud losses, false positives, action rate reductions, merchant integrations, and operational efficiency.
• Extract data from the warehouse, back-testing models, comparing model performance, and communicating findings.
• Develop end-to-end solutions that address business and engineering challenges related to funnel optimization, 3rd party integrations, fraud mitigation, user engagement, and overall user experience.
• Design optimal risk actions that strike a balance between growth and risk controls.
• Collaborate with cross functional teams to identify and monitor risk associated with new products, marketplaces, and payment methods.
• Supervise at least 1 Data Scientist / 15-2051; Data Scientists.
• May telecommute.
  

Basic Qualifications

• Employer will accept a Bachelor's degree in Mathematics, Bioinformatics, Statistics, Engineering (Any), Computer Science, or related field, followed by five years of progressive, post-baccalaureate experience in the job offered or in a related occupation.
• Position requires:
  • Data science and visualization tools: SQL, Python, R, Tableau, and Google Studio;
  • Experimentation techniques including A/B testing;
  • Statistical analysis including descriptive statistics, correlation, regression, and confidence intervals;
  • Quantitative modeling including machine learning models, time-series forecasting, and casual impact analyses;
  • Developing, utilizing, and communicating relevant metrics;
  • Experience with large data sets, mining actionable insights, statistical methods, and basic ML model development;
  • Experience in Risk/Fraud/Payments;
  • Designing decision frameworks for optimizing fraud loss vs. conversion / user friction trade-off.

Ready to Ride?