Data Scientist, Mobile App Performance

About the Role:

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.

What You'll Do:

Model  Development  

• 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 

Drive Product Development  

• 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. 

Evaluate and Improve System Health 

• 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 

Who You Are:

Required 

• 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 

Strongly Preferred 

• 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 

Additional Information

Return to Office: PubMatic employees throughout the globe have returned to our offices via a hybrid work schedule (3 days “in office” and 2 days “working remotely”) that is intended to maximize collaboration, innovation, and productivity among teams and across functions.

Benefits: Our benefits package includes the best of what leading organizations provide such as, paid leave programs, paid holidays, healthcare, dental and vision insurance, disability and life insurance, commuter benefits, physical and financial wellness programs, unlimited DTO in the US (that we actually require you to use!), reimbursement for mobile and fully stocked pantries plus in-office catered lunches 5 days per week.

Diversity and Inclusion: PubMatic is proud to be an equal opportunity employer; we don’t just value diversity, we promote and celebrate it. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status

About PubMatic

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.

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