Technical Product Manager

• 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.

Measurement Lifecycle Management — Accelerated with AI

• 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.

Identify & Prioritize AI Use Cases Within Measurement

• 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.

Build, Prototype & Scale AI Solutions for Measurement

• 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.

• 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.

• 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.

• 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

• 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.

• 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

• Bachelor's degree or equivalent experience in a quantitative, business or related field.

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.