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About This Role
Technical Product Manager (Contract)
Marketing Measurement \& AI Enablement
Team: Unilever Prestige — Portfolio Marketing Office (Paid Media \& Measurement Excellence)
Reports to: Head of Paid Media \& Measurement Excellence, Unilever Prestige
Location: US (NY / LA)
Type: Full\-time
About Unilever Prestige
===========================
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.
Role Overview
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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.
What You'll Do
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1\. Own Marketing Measurement as a Product
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- 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.
2\. Data \& Pipeline Coordination
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- 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.
3\. Measurement Lifecycle Management — Accelerated with AI
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- 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.
4\. Identify \& Prioritize AI Use Cases Within Measurement
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- 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.
5\. Build, Prototype \& Scale AI Solutions for Measurement
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- 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., LangGraph, 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 productionize what works — with clear inputs, outputs, success metrics and a plan to maintain it beyond the pilot.
6\. Product Experience \& Outputs
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- Own how measurement outputs — and the AI tools that interpret them — are delivered and used.
- Partner on dashboards, planning tools and budget allocation frameworks (e.g., Power BI media dashboards, MMM scenario planners, AI\-driven measurement copilots embedded in those tools).
- Ensure outputs are clear, actionable and aligned to brand and portfolio workflows.
7\. Drive Adoption \& Enablement
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- Drive adoption of the measurement stack — both the underlying models and the AI tools layered on top — across brands through training, playbooks and embedded workflows.
- Educate stakeholders on interpretation and limitations of both measurement outputs and AI assistance; reconcile and communicate differences between MMM, platform reporting and other approaches.
- Track adoption and outcomes; iterate until measurement (with AI as the enabler) is genuinely embedded in how investment decisions get made.
8\. Insights \& Business Impact
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- Translate measurement outputs into clear recommendations for investment decisions, using AI to scale the speed and consistency of those recommendations across brands.
- Partner with brand marketing teams to inform budget allocation and channel strategy.
- Track the impact of measurement\-driven decisions over time
What We're Looking For
==========================
Core Requirements
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- 6–10\+ years in product management, marketing analytics, marketing science or a closely related technical/analytics role.
- Proven, hands\-on experience building AI or LLM\-based solutions applied to analytics, measurement or decision\-support — not just familiarity with the concepts.
- Comfortable in Python, working with APIs and data pipelines.
- Track record of taking measurement and/or AI ideas from concept to working, adopted solution.
Skills
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- Strong understanding of paid media and performance marketing.
- Strong understanding of marketing science principles (MMM, attribution, incrementality) and how they apply to real\-world decision making; hands\-on experience with at least one strongly preferred.
- Strong data fluency including SQL, BI tools (Power BI or Tableau), and familiarity with BigQuery or Databricks and Python or R.
- Hands\-on experience building AI agents or agentic workflows (e.g., LangGraph, CrewAI, AutoGen or similar) strongly preferred.
- Working knowledge of LLMs, prompt design, embeddings and/or retrieval\-augmented generation (RAG) — used to surface, explain or operationalize analytical outputs.
- Hands\-on experience
Education
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- Bachelor's degree or equivalent experience in a quantitative, business or related field.
A Note on This Role
=======================
This is a hands\-on builder role with a tight remit. You will own measurement end\-to\-end and personally build the AI that makes it work harder — but AI here is a means, not an end. This role does not own AI strategy, AI guardrails or AI use cases outside marketing measurement. The right candidate is equally comfortable in a brand CMO meeting, an MMM model review and a Databricks notebook.
This is a fully remote role with Dermalogica as the employer and on its employment terms. The expected annual base salary range for this position is $120K to $140K. The exact base salary is determined by various factors including experience, skills, education, and budget.
The role is slated to run minimum of one year with reassessment for the second year as a permanent position or possible alignment with other opportunities within Unilever Prestige.
Apply now and become a key contributor to the Unilever Prestige growth trajectory!
*Dermalogica is an equal opportunity employer committed to fostering an inclusive culture where all employees are valued, supported, and empowered to succeed.*
Salary Context
This $120K-$140K range is in the lower quartile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Dermalogica, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 40% below the category median. Disclosed range: $120K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Dermalogica AI Hiring
Dermalogica has 1 open AI role right now. They're hiring across AI Product Manager. Based in New York, NY, US. Compensation range: $140K - $140K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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