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Haus

Incrementality measurement and synthetic-control experiments platform

Marketing Measurement Specialist - MMM

Data ScienceConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 4 years of direct marketing mix modeling experience, including some model building, full interpretation, and recommendation-making.
  • Experience with incrementality testing or other marketing experimentation and a general understanding of the measurement landscape.
  • At least 4 years of experience in a customer-facing role.
  • Ability to navigate messy media data and translate complex models into actionable campaign decisions.
  • Ability to manage multiple customers while progressing longer-term process-building work in an ambiguous, early-stage environment.
  • Experience communicating product feedback to technical teams.
  • Understanding of how serving causal marketing mix modeling at scale differs from traditional marketing mix modeling, including update cadence, experiments as model inputs, and working in support of a client-owning Measurement Strategy Manager.

Responsibilities

  • Partner with Measurement Strategy Managers throughout the causal marketing mix modeling journey, from kickoff and method presentation through model release, interpretation, in-app enablement, and action planning, translating results into planning and budgeting improvements across channels and markets in the customer's business context.
  • Configure and quality-assure initial model inputs and outputs, review model updates, and explain method, model, and scope changes and multi-key performance indicator insights.
  • Own escalations during value-realization moments, including business reviews, major planning sessions, handoffs, meaningful method, model, or scope changes, complex causal marketing mix modeling questions, custom covariates, and technical re-engagement after customer-team changes; escalate to causal marketing mix modeling Data Science when needed.
  • Deliver high-quality, on-time models and follow-ups, including deep-dive prototypes, scenario work, before-and-after narratives, and renewal narratives, to support account expansion while meeting onboarding and modeling service-level agreements across a sizable book of business.
  • Define effective marketing mix modeling engagement with Measurement Strategy Managers and build playbooks, templates, and documentation for repeatable causal marketing mix modeling workflows.
  • Design and supervise artificial intelligence workflows for causal marketing mix modeling, turning analyses into guardrails and training data to safely automate repeatable tasks and focus effort on high-value prototypes and complex enterprise challenges.
  • Bring structured customer feedback to product, science, and engineering teams and partner on the roadmap.
  • Support case studies, bakeoffs, and pre-sales when subject-matter expertise is needed.

Desired Qualifications

  • Agency experience.
  • Experience making marketing mix modeling value tangible to clients and ideas for doing so at Haus.
  • Scalable approaches to data collection, covariate selection, and insights delivery, with intentionality behind any automation built.
  • A perspective on how a marketing-mix-modeling-first incrementality testing roadmap differs from a GeoLift-first roadmap, including test sequencing, channel prioritization, and experiment design.
  • A framework for conveying different confidence levels across model outputs given uneven experimental coverage by channel, and how that informs recommendations.
  • Experience educating marketing attribution-native clients about marketing mix modeling and a perspective on how marketing mix modeling, marketing attribution, and experiments fit together in a modern measurement stack.

About the Company

Haus

Incrementality measurement and synthetic-control experiments platform

Haus is a marketing science platform that helps businesses measure the incremental impact of their marketing campaigns and allocate budgets more efficiently. It uses modeling to create synthetic control groups, enabling multi-cell experiments and the generation of a marginal return curve to show how much sales were driven by campaigns that would not have occurred otherwise. The platform can measure incrementality across channels by integrating with data from sales channels like Amazon and retail sources, providing a comprehensive view of marketing performance. It is designed for marketers of all levels, from beginners to experienced practitioners. Unlike some alternatives, Haus relies on synthetic controls and multi-cell experiments to estimate incrementality and to validate and refine strategies for launching new channels, with the goal of helping users optimize marketing spend and maximize growth.

Company size
201-500
Company stage
Early VC
Total funding
$61.1M
Headquarters
San Francisco, CA
Founded
2021

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