Technical Product Manager Marketing Measurement & AI Enablement

Current job opportunities are posted here as they become available.

Reports to: Head of Paid Media & Measurement Excellence, Unilever Prestige

Location: US (NY / LA)

Type: Full-time

Unilever Prestige is the luxury beauty division of Unilever, managing a high-growth portfolio of premium skincare, haircare and makeup brands (Dermalogica, Tatcha, Hourglass, Paula's Choice, K18, Murad, Living Proof, Garancia). The Portfolio Marketing Office is a lean Center of Excellence designed to raise the bar, accelerate transformation, spread what works faster, and safeguard long-term brand health across the portfolio — without shadowing the brand teams.

Media & Measurement is the one area where the portfolio plays an operator role, given the scale and infrastructure advantage of running MMM, attribution and the AI tooling that powers them once for the whole portfolio rather than brand-by-brand.

We're hiring a Technical Product Manager to own marketing measurement as a product for the Prestige portfolio — and to use AI as the lever that makes that measurement faster, smarter and more usable.

This is a hybrid PM + hands‑on builder role. You will own the MMM, attribution and incrementality stack end‑to‑end, and you will personally prototype and ship the AI agents and workflows that turn measurement outputs into decisions the brands actually act on. AI here is not a separate workstream — it is how measurement gets built, refreshed, interpreted and adopted.

This is not a model‑development‑only role, not a strategy‑only role, and not a general AI‑transformation role. The scope is firmly inside marketing measurement.

• Act as the central owner of marketing measurement across the Prestige portfolio.
• Define and manage a single roadmap that covers MMM, attribution and incrementality testing and the AI capabilities that power them — prioritized by business impact and scalability.
• Balance tradeoffs between rigor, usability and speed.
• Partner with data engineering to ensure reliable, standardized inputs across media, revenue, promotions and external drivers — the foundation both the models and any AI tooling depend on.
• Monitor data quality and resolve upstream issues before they reach the models or any AI layer built on top of them.

• Own refresh cadence, versioning and recalibration strategies for MMM, attribution and incrementality.
• Use AI to automate and accelerate the measurement lifecycle itself — e.g., agents that QA inputs, flag drift, automate model refresh runs, reconcile MMM vs. platform vs. incrementality results, and surface anomalies for human review.
• Ensure outputs remain accurate, stable and aligned with business reality, with AI augmenting (not replacing) analytical judgment.

• Translate ambiguous measurement pain points into clearly scoped AI use cases (e.g., a copilot for media planners to query MMM, an agent that explains why platform attribution disagrees with MMM, automated narrative generation for measurement readouts).
• Prioritize based on business value, feasibility and data readiness — and say no to anything outside the measurement remit.

• Personally design and build AI agents and agentic workflows that operationalize measurement — e.g., MMM/attribution reconciliation copilots, budget reallocation recommenders grounded in MMM outputs, scenario‑planning assistants, automated insight summaries for brand teams.
• Prototype quickly using agent frameworks (e.g., Lang Graph, CrewAI or similar), LLM APIs (OpenAI, Claude, etc.) and lightweight front‑end tooling (e.g., Gradio, Chainlit).
• Validate cheaply, then partner with Data Science & Analytics and IT to product ionize what works — with clear inputs, outputs, success metrics and a plan to maintain it beyond the pilot.

• Own how measurement outputs — and the AI tools that interpret them — are delivered and used.
• Partner on dashboards, planning…