Data Scientist II, Tech
UberSan Francisco, United States$140k – $172kPosted 21 March 2026
Tech Stack
Job Description
Data Scientist II, Tech
Department: Data Science
Team: Data Scientist
Location: San Francisco, United States
Type: Full-Time
**Employer:**
Uber Technologies, Inc.
**Job Title:**
Data Scientist II, Tech
**Job Location:**
San Francisco, California
**Job Type:**
Full Time
**Rate of Pay:**
$140,000 to $172,000 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](https://www.uber.com/careers/benefits).
**Duties:**
Leverage data to perform intensive analysis across all areas of business to drive product development. Design experiments and analyze results to draw conclusions. Perform time-series analyses, hypothesis testing, and causal analyses to statistically assess relative impact and extract trends. Build models to enhance understanding of user behavior and predict future performance of cohorts. Create dashboards and reports to regularly communicate results and monitor key metrics. Extract data from warehouse, back-test the model, and compare the model performance and communicate the results. Develop end-to-end solutions to abstract business and engineering problems related to funnel optimization, user engagement, and user experience. May telecommute.
Employer will accept a Master's degree in Engineering (Any), Information Technology, Information Management, Information Systems, Mathematics, Statistics, or related field and two years of experience in the job offered or related occupation.
Position requires:
1. R or Python;
2. Database query languages: SQL;
3. Experimentation techniques including simulation or A/B testing;
4. Statistical analysis including descriptive statistics, correlation, regression, or confidence intervals;
5. Developing relevant metrics;
6. KPIs to measure performance by product teams;
7. Quantitative modeling including machine learning models, time-series foreca
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