Drimva

Newton Research

Principal Product Manager – Agentic Media Measurement

$180K–$230K

Greater Boston Area +2 · Principal · Full-time · Posted Apr 22, 2026

Location
Greater Boston Area
Needham, Massachusetts, United States
New York, New York, United States
Experience
5–8 years in marketing measurement, analytics, or marketing science; 5+ years in product management, including 2+ years building measurement, analytics, or AI/ML products at scale.

About the job

Owns the strategy, roadmap, and delivery of Newton Research’s agentic media measurement platform, turning marketing science into insights that guide media investment and autonomous optimization.

What you'll do

  • Lead product direction for MMM, MTA, incrementality testing, and cross-channel measurement.
  • Work with data science and customers to shape rigorous modeling approaches and turn workflows and feedback into product requirements.
  • Own product delivery and iteration, including prototyping, KPI tracking, go-to-market collaboration, and cross-functional execution.

What they ask for

  • 5–8 years of hands-on experience in marketing measurement, analytics, or marketing science, including direct work with MMM, MTA, incrementality testing, or media effectiveness methodologies.
  • 5+ years of product management experience, including 2+ years building measurement, analytics, or AI/ML products at scale.
  • Understanding of statistical modeling, including Bayesian methods, causal inference, regression, and time-series analysis.
  • Experience with multi-channel media environments and practical measurement challenges such as data gaps, attribution bias, privacy-related signal loss, and walled garden limitations.

Nice to have

  • Experience with agency measurement teams, brand analytics groups, or marketing science consultancies.
  • Passion for marketing science, media analytics, and the adtech/martech ecosystem.

Pay

$180,000-$230,000 USD base salary

Tools

MMM, MTA, Bayesian MMM, causal inference, PCMCI+, adstock modeling, Agile

AI focus

Builds agentic measurement capabilities using AI/ML and causal techniques, connecting measurement insights to autonomous media optimization.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 6, 2026 (UTC)