Data Scientist, Mobile App Performance

Redwood City · USD 225,000-250,000 · 5+ years experience · Hybrid · Posted Oct 9, 2026

At a glance

PubMatic is one of the world’s leading scaled digital advertising platforms, offering more transparent advertising solutions to publishers, media buyers, commerce companies and data owners, allowing them to harness the power and potential of the open internet to drive better business outcomes.
Founded in 2006 with the vision that data-driven decisioning would be the future of digital advertising, we enable content creators to run a more profitable advertising business, which in turn allows them to invest back into the multi-screen and multi-format content that consumers demand.

Own the data science function for Mobile App performance, working at the intersection of ML and programmatic advertising.
This is a high-ownership, high-visibility role--both internally and with our customers.

Build signal enrichment pipelines leveraging contextual signals, behavioral data, and mobile attribution frameworks
Develop production-grade predictive models based on event data (clicks, installs, in-app events, retention).
Partner with GTM teams to translate metric frameworks into buyer-facing insights and predictive models
Conduct deep-dive analysis to influence product and engineering decisions: pull and query large datasets, extract valuable signals from noise, and produce actionable insights into how we can drive better outcomes for buyers and publishers
Develop predictive modeling frameworks to quantify performance gains from product hypotheses before they ship — reducing decision latency and de-risking the roadmap
Act as the senior data science voice in product discussions. Bring partner-level insight and analytical rigor to roadmap decisions.
Understand changes and drivers across PubMatic's mobile ecosystem: diagnose issues, identify structural inefficiencies, and track input/output metrics at each system component
Work with product and engineering to define success metrics, design experiments, and evaluate new features before and after launch.
Design experiment frameworks and causal inference methods that produce reliable, unbiased signals for product and business decisions

5+ years of hands-on data science experience, with a focus on ML model development, statistical inference, and production-grade analysis
Deep fluency in Python and SQL; experience with large-scale data environments (Spark, Hadoop, Hive, or equivalent)
Strong foundation in experimental design, hypothesis testing, regression modeling, and causal inference
Experience with ML algorithms relevant to ranking, prediction, and optimization problems: gradient boosted trees, deep learning, k-means, and related methods
Proven ability to extract actionable signals from noisy, high-volume datasets and communicate findings clearly
Track record of owning projects end-to-end, from problem framing through to production impact

Experience in mobile advertising, programmatic, or ad tech
Familiarity with mobile measurement and attribution or campaign performance optimization in mobile applications
Background working alongside ML engineering or product teams on systems where the model is the product
Advanced degree (Master's or PhD) in Statistics, Computer Science, Mathematics, or a related quantitative discipline