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Based on 240 similar postings.
Employer
About Uber
Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.
The Sr Manager, Applied Science leads a high-performing team of Applied Scientists within the Marketing department to quantify the impact of global marketing investments for Mobility and Delivery platforms. This role involves translating ambiguous business challenges into scientific roadmaps, building a core measurement engine, and deploying a next-generation marketing science stack. Key responsibilities include leading geo-experiments, conversion lift studies, and synthetic controls while triangulating measurement with Marketing Mix Modeling and algorithmic attribution. The successful candidate will utilize Python, SQL, and modern AI/ML frameworks to integrate predictive modeling and LTV modeling into production systems. The work focuses on establishing scientific ground truth for marketing efficiency, optimizing budget allocation, and providing actionable analytical frameworks to solve complex problems in media optimization and digital ad platforms.
Translate ambiguous business challenges into scientific roadmaps, experiment plans, and analytical frameworks.
Build and scale geo-experiments, conversion lift studies, and synthetic controls to measure marketing efficiency.
Calibrate Marketing Mix Modeling (MMM) and multi-touch attribution models against experimental results.
Coach and lead a team of Applied Scientists to ensure scientific rigor and execution speed.
Integrate modern AI, predictive ML, and LTV modeling into production systems to optimize budget allocation.
Translate complex statistical methodologies into actionable decision frameworks for Marketing, Media, and Engineering leaders.
What we're looking for
7+ years of experience 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, including geo-experiments and synthetic controls.
Strong foundation in machine learning, predictive modeling, and applied AI in enterprise environments.
Proficiency in Python, SQL, modern AI/ML frameworks, and big data technology.
Exceptional executive communication skills to translate complex statistical concepts for business stakeholders.
Ph.D. or M.S. in Statistics, Econometrics, Economics, Computer Science, Machine Learning, Operations Research, or a related quantitative field (preferred).
5+ years of domain experience in AdTech/MarTech, media optimization, and digital ad platforms (preferred).
Large Language Models
Deep Learning
Information Retrieval
Agent Orchestration
Machine Learning
Retrieval-Augmented Generation
Prompt Optimization
Fine-tuning
Domain Adaptation
Data Mining
Program Synthesis
AI Evaluation
Telemetry
Production Deployment
Statistics
Predictive Analytics