Technical Product Manager (Contract) - Marketing Measurement & AI Enablement at Dermalogica in Los Angeles, CA

Job description

• 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.

Responsibilities

• 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., 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

Requirements

• 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.

Skills

Required

• Python
• APIs
• Data pipelines
• SQL
• BI tools (Power BI or Tableau)
• BigQuery or Databricks
• Python or R

Benefits

• 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.