Director, Product Management, Analytics

PubMatic•Redwood City, CA

3d•$225,000 - $275,000•Hybrid

About The Position

You will own the observability program and the product area behind it: business intelligence, agentic analysis, and workflow automation. Your job is to find the use cases that change how PubMatic operates, then get them built and adopted. 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.

Requirements

8+ years in product management, with a record of Director-level scope and impact (owning a product area, not a feature).
Shipped data, analytics, or BI products used for daily decisions, with measurable adoption.
Hands-on experience with LLM or agentic systems in production: tool use, retrieval, evaluation, failure analysis, and cost and latency trade-offs.
Technical enough to build prototypes yourself: strong SQL, working Python or equivalent, and fluency with large-scale data stacks (warehouses, streaming, semantic layers).

Nice To Haves

Ad tech or marketplace experience (SSP, DSP, ad exchange).
Experience with observability or monitoring products (data quality, anomaly detection, alerting, SLOs).
Background in forward-deployed engineering, solutions engineering, data science, or consulting.
Experience building internal platforms where the customers are colleagues.

Responsibilities

Own the strategy, roadmap, and outcomes for observability, agentic analysis, and workflow automation.
Identify high-value use cases across the business, size the impact in revenue, margin, or hours returned, and kill the ones that don’t clear the bar.
Prototype directly (SQL, notebooks, agent frameworks, automation tools) to prove value before asking for engineering capacity.
Define the production path: data foundations, evaluation, guardrails, monitoring, and adoption for agentic systems.
Partner with Engineering and Analytics to build and ship. Make clear trade-offs on build vs. buy and on what to stop.
Set metrics for adoption and impact and report them to executive leadership.
Lead and develop a small team of product managers and analysts.