Uber
Sr Manager, Applied Science
$252K–$280K
New York City, New York +1 · Manager · Full-time · Posted Oct 20, 2026
- Location
- New York City, New York
San Francisco, California - Experience
- 7+ years in Applied Science, Quantitative Research, or Machine Learning; 3+ years of direct people management. Preferred: 5+ years of AdTech/MarTech domain experience.
- Education
- Preferred: Ph.D. or M.S. in Statistics, Econometrics, Economics, Computer Science, Machine Learning, Operations Research, or a related quantitative field.
About the job
Leads an applied science team building marketing measurement and AI/ML systems to assess and optimize marketing investments across Uber’s Mobility and Delivery platforms.
What you'll do
- Set scientific roadmaps and experiment plans for business challenges; lead measurement approaches including geo-experiments, conversion lift, synthetic controls, MMM, and attribution.
- Manage and develop Applied Scientists, while partnering with Marketing, Media, Operations, and Engineering leaders.
- Integrate AI, predictive ML, and LTV modeling into production systems for budget allocation and automated decision-making.
What they ask for
- 7+ years in Applied Science, Quantitative Research, or Machine Learning in tech or ad-tech.
- 3+ years of direct people management experience building and leading science teams.
- Deep expertise in causal inference and experimentation at scale; strong foundation in machine learning, predictive modeling, and applied AI.
- Proficiency in Python, SQL, modern AI/ML frameworks, and big data technology.
Nice to have
- Ph.D. or M.S. in a related quantitative field.
- 5+ years in AdTech/MarTech, media optimization, and digital ad platforms; experience scaling geo-testing or conversion lift frameworks and calibrating MMM or attribution with experimental data.
Pay
USD $252,000 per year - USD $280,000 per year (base salary, New York City, NY); USD $252,000 per year - USD $280,000 per year (base salary, San Francisco, CA)
Tools
Python, SQL, Marketing Mix Modeling (MMM)
AI focus
Integrates modern AI, predictive ML, and LTV modeling into production systems to optimize budget allocation and automated decision-making.
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