Dhaval Khamar
Product leader · AdTech · AI · Data platforms

AI and measurement products advertisers can trust.

I lead Audiences, Measurement & AI at Yahoo DSP: a team of six PMs shipping AI agents, privacy-safe CTV and retail media measurement, and the ads data platforms underneath them.

See selected work Get in touch
signal_from_noise · fit(model)R² 0.00
raw data insight decision
20+
Years turning complex workflows into software
12
GA launches in 2025–26
$32M
A year cut from the DSP's AWS bill
6
Product managers led, with 5 engineering partner teams
Built atYahooCapgeminiOracle
01 · About

From engineer to AdTech product leader

I'm a product leader who started as an engineer and has spent 20+ years turning complex professional workflows into software.

At Yahoo, I lead a team of product managers across the DSP Audiences, Measurement & AI product line. We've shipped LLM-powered search, a natural-language assistant and agent interfaces (MCP and agent-to-agent) that ad traders use every day. Before that, I ran Yahoo's enterprise BI and machine-learning roadmap for finance.

Before product, I spent 14 years in consulting at Oracle and Capgemini, leading M&A integration, large programs and process consulting for enterprise clients. At Yahoo I also delivered launches like Yahoo Backstage (+$12M revenue), Blueprint (+$2.3M) and Creative Studio (+$1.3M), and an AWS data-platform migration that cut operating cost by about 30%.

My background runs deep in programmatic advertising: DSP workflows, audience targeting, CTV and retail media measurement, and multi-touch attribution. I find the real problem with customers and engineers, write the strategy and specs, size each bet with a P&L model, and stay hands-on through launch.

02 · How I lead

Four principles I build by

01

Foundations before AI

My 2026 strategy at Yahoo DSP sequences the work: data foundations, then speed, then AI-driven discovery, then intent signals. AI built on data nobody trusts doesn't get used.

02

Check the plumbing first

An audit found only 13K of 394K conversion rules were active. Before debating strategy or models, I verify the data a product runs on actually does what everyone assumes.

03

Every bet gets a P&L

No major initiative enters the quarterly plan without a P&L model, so revenue and advertiser-value trade-offs are explicit before engineering starts.

04

Guardrails are product features

Blocklist checks, bounded outputs and automatic labeling ship in version one. Privacy-safe patterns like k-anonymity and compute-to-data are designed once and reused.

03 · Focus areas

Three dimensions, one practice

dim_01 / AI

AI Agents & LLM Products

Audience Copilot, Smart Audience Search, AI-written descriptions at scale and a troubleshooting agent, with guardrails designed in: blocklist checks, bounded outputs and automatic labeling.

  • LLM apps
  • AI agents
  • MCP · A2A
  • Semantic search
  • Claude on Bedrock
dim_02 / BI

BI & Measurement

From Yahoo's enterprise finance BI and Poseidon Analytics, the platform behind Yahoo DSP's billing and reporting, to ads measurement: conversion attribution and clean-room CTV measurement.

  • Enterprise BI
  • Looker MCP
  • Attribution · MTA
  • CTV measurement
  • Retail media
dim_03 / Data

Data Platforms

The foundation underneath: ads data pipelines and lakes, privacy-safe data collaboration, purchase signals for bidding, and AWS cost optimization that took the DSP's yearly bill from $72M to $40M.

  • Clean rooms
  • k-anonymity
  • Streaming (Flink)
  • AWS cost optimization
04 · Experience

From engineer to product leader

role · company
200020052010201520202026
Product Management Lead, Audiences, Measurement & AIYahoo DSP · San Francisco
2021 – now
Principal Product Manager, Enterprise BIYahoo
2017 – 21
Program Manager · Principal Architect · M&A integration leadCapgemini · Chicago, Kansas City, Bangalore
2007 – 2017
Senior Consultant, Oracle FinancialsOracle
2003 – 07
Senior ProgrammerApplitech Solutions
2000 – 03
05 · Selected work

Problem → approach → impact

Project · Yahoo DSP

Smart Audience Search

Hybrid semantic and keyword search, now the default in Audience Explorer.

Problem

Ad traders could only find audiences by exact name in a catalog of about 4.8 million segments.

Approach

Search over LLM-generated descriptions, backed by a backfill that raised description coverage from about 15% to every new and existing audience.

12K+

searches from 327 advertiser seats in 14 weeks, 0.57s median response

Project · Yahoo DSP

AI troubleshooting agent

An agent for the conversion-attribution pipeline.

Problem

An audit found only 13K of 394K conversion rules were active, and finding the root cause of a data issue took days.

Approach

Fixed mis-credited conversions for 46 advertisers, set a next-day, 99.5%-accuracy target and introduced an AI agent for diagnosis.

71h→5h

root-cause analysis time

Project · Yahoo DSP

Netflix CTV clean-room measurement

Privacy-safe attribution for connected-TV ads.

Problem

Advertisers couldn't measure Netflix CTV campaigns without moving user-level data.

Approach

Clean-room attribution using k-anonymity and compute-to-data, built as a pattern now being reused for Disney, Walmart and Spotify.

61

advertisers at GA, Sep 2026

Project · Yahoo DSP

Poseidon Analytics & AI on BI

The billing, data warehouse and analytics platform behind Yahoo DSP.

Problem

Separating Yahoo's ads business from its consumer systems meant standing up separate billing and analytics, with reporting spread across four disparate systems.

Approach

As product manager, I led Poseidon Analytics, which generates all of Yahoo DSP's $2B+ in billing and partner payment reporting. We consolidated four analytics systems into it, deduplicated data and defined clear metrics, then launched an MCP server on its Looker instance in May 2026 so people can query data without logging in to Looker.

4→1

analytics systems, $1M+ in savings, and 30% higher BI adoption with Looker MCP

Project · Yahoo DSP

DSP AWS cost optimization

Bringing the cost of running the DSP in line with its revenue.

Problem

Running Yahoo DSP on AWS cost $72M a year, a direct hit to the platform's margin.

Approach

Two lenses. Technical: mapped each DSP product to business components, technology components and AWS services, then cut data retention left behind by years of growth (about 20% saved). Business: bid-volume analysis showed 20–30% of lower-funnel bid requests could be dropped with negligible impact (about 15% more).

$72M→
$40M

yearly AWS cost, about $32M saved each year

Project · Yahoo

Enterprise BI & ML roadmap

Finance analytics and predictive support.

Problem

Yahoo's finance organization needed one roadmap for BI and machine learning that delivered measurable savings.

Approach

Built and delivered the finance BI and machine-learning roadmap, including ML-based predictive analytics. Delivered $1.25M in cost savings.

+200%

BI usage, with 60% fewer support tickets

06 · Consulting & transformation

Before product: M&A, programs and process

Before moving into product, I spent 14 years in consulting at Oracle and Capgemini, across Bangalore, Kansas City and Chicago. That's where I learned to run large programs, untangle business processes and deliver against a hard number.

M&A integration · Capgemini

$140M

Annual savings from leading the technology side of the Motorola Wireless spin-off and its integration with Zebra Technologies, a $4B deal. Delivered on schedule and 20% under budget, against a $105M savings target.

Program management · Capgemini

$3M → $10M

Grew a key client account to $10M a year as Program Manager, leading delivery teams of up to 30 across onshore and offshore locations.

Solution architecture · Capgemini

$10M

Revenue from a project-billing product I designed for engineering firms, adopted by Black & Veatch, AECOM and Burns & McDonnell. It won the iGATE Innovator of the Year award in 2014.

Process consulting · Oracle

Oracle Financials

Collections, receivables, payables and expenses implementations for Cell C and Mutual & Federal, plus data migration and a customer loyalty program for UK Co-operatives.

07 · Writing

Notes from the field

  • Substack · Nov 2026
    Retail media · Measurement

    Closed Loop Isn't the Same as True

    Retail media fixed attribution's data problem, not its counterfactual problem. Why closed-loop ROAS overstates some tactics, how ROAS-optimizing bidders drift toward shoppers who would have bought anyway, and what networks should change.

  • Substack · Sep 2026
    AI adoption

    The Model Isn't the Bottleneck. Your Data's Reputation Is

    An essay on AI adoption: AI projects stall when people stop trusting the data underneath them, and that trust erodes much faster than it builds.

  • Yahoo · Apr 2026
    Agentic AI · Data

    Why Data Is the Queen in the Agentic AI Era

    Co-written with Srinivas Bhagavatula. An AI agent is only as dependable as the data underneath it, so accurate, governed, real-time data matters more than an impressive demo, with Yahoo DSP's identity framework, open-standard integrations and human approval controls as the example.

Subscribe on Substack

08 · Toolkit

What I work with

AI products

  • LLM applications
  • AI agents, MCP & A2A
  • Semantic search
  • Guardrail design

Ads & measurement

  • Programmatic (DSP)
  • Audience targeting
  • CTV & retail media
  • Multi-touch attribution

Data & platforms

  • Pipelines & data lakes
  • Clean rooms
  • Streaming (Flink)
  • AWS

Consulting & delivery

  • M&A integration
  • Program management
  • Process consulting
  • Client account growth

Product craft

  • Customer discovery
  • Strategy memos & PRDs
  • P&L and scenario models
  • Coaching PM teams

Education & certifications

  • B.E., Electronics & Communications · Dharmsinh Desai University
  • Mini-MBA, General Management · Loyola University Chicago
  • Deep Learning Algorithms and Applications · UC Santa Cruz
  • Product Management Certificate · General Assembly  ·  Certified Scrum Product Owner (CSPO) · Scrum Alliance
09 · Contact

Let's talk about what your data can prove.

I'm open to senior product leadership roles, advisory work and speaking. Topics I'm glad to dig into:

  • Retail media incrementality
  • AI agents in ad platforms (MCP, A2A)
  • Clean-room CTV measurement
  • Data strategy before AI
  • Building PM teams for data products
Emaildhaval.khamar@gmail.com LinkedInlinkedin.com/in/dhavalkhamar Substackdhavalkhamar.substack.com LocationSan Francisco, CA Résuméavailable on request