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.
What you’ll do
Design and execute large-scale experiments, such as A/B tests, to evaluate product features or model updates and draw detailed, impactful conclusions.
Conduct deep-dive statistical and ad-hoc analyses to uncover trends and patterns that shape product strategy and respond to real-world market shifts.
Architect strategic metrics and monitoring systems while collaborating with Engineering to overhaul the underlying data pipelines that ensure data integrity.
Translate complex business needs into rigorous analytical frameworks and data requirements in partnership with Engineering, Product, and Operations.
Drive operational excellence by building scalable dashboards that provide visibility into underlying business drivers and product success.
Mentor junior data scientists and provide guidance on best practices for experimental design, exploratory analysis, and data storytelling.
Time spent in the day
45% Querying complex datasets with SQL and performing statistical analysis in Python or R
20% Designing, running, and analyzing A/B experiments to evaluate product changes
15% Collaborating with cross-functional stakeholders to align on requirements and strategic priorities
10% Building and maintaining visualization dashboards to track key performance metrics
10% Presenting findings to senior management and mentoring junior team members
Basic Qualifications
Minimum 5 years of professional experience in a data analytics-focused role with a proven track record of turning ambiguous business needs into actionable analyses.
Advanced expertise in SQL and Python or R, with the ability to write clean, documented pipelines for data wrangling and transformation.
Extensive experience in experimental design, including the proven ability to design, run, and interpret large-scale A/B tests.
Solid theoretical foundation in causal inference or complex experimental designs dealing with network effects and rare events.
Solid proficiency in probability theory and statistical techniques, including MLE, parameter estimation, and methods for truncated/censored data.
Exceptional communication and stakeholder management skills with a history of influencing leaders and navigating cross-functional relationships.
BA/BS, M.S., or Ph.D. in Statistics, Economics, Math, Computer Science, or a related quantitative field (or equivalent professional experience).
Preferred Qualifications
Hands-on experience applying causal inference methods (e.g., matching, IV, DID) to real-world datasets to drive global product strategy.
Experience with big data tools such as Hive, Spark, or Presto for large-scale data processing in a production environment.
Proven ability to manage complex, multi-stakeholder projects while maintaining extreme accuracy under tight timelines.
Grit and a strong sense of ownership, with the ability to navigate the technical nuances of a dynamic marketplace.
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