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Data Scientist II, Tech

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Uber
Full Time position
Listed on 2026-07-01
Job specializations:
  • IT/Tech
    Data Analyst, Data Scientist, Data Engineering
Salary/Wage Range or Industry Benchmark: 140000 - 172000 USD Yearly USD 140000.00 172000.00 YEAR
Job Description & How to Apply Below

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 compensation. You will also be eligible for various benefits. More details can be found at the following link

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.

Education & Experience

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
  • R or Python
  • Database query languages: SQL
  • Experimentation techniques including simulation or A/B testing
  • Statistical analysis including descriptive statistics, correlation, regression, or confidence intervals
  • Developing relevant metrics
  • KPIs to measure performance by product teams
  • Quantitative modeling including machine learning models, time‑series forecasting, or causal impact analyses
  • Dashboarding/data visualization:
    Google Data Studio, Tableau, Mixpanel, or Looker
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