Senior Applied Scientist - Research, cMMM

Haus is the incrementality platform leading brands trust to optimize billions in ad spend worldwide. Using frontier causal inference-based econometric models to run experiments, we help brands measure the business impact of marketing, pricing, and promotions with scientific precision. Over $360B is spent annually on paid advertising in the US alone, and the famous quote “half the money I spend on advertising is wasted; the trouble is I don't know which half” still rings true. Haus helps marketers identify which half, and reallocate it to maximize growth.

With a founding team of former product managers, economists, and engineers from Google, Netflix, Meta, and Amazon, we make high-quality decision science, incrementality testing, and causal marketing mix modeling accessible to businesses of all sizes—automating the heavy lifting of experiment design, data processing, and insights generation. Haus works with leading brands like FanDuel, Sonos, and Dr. Squatch, delivering ROI gains as high as 30x.

Haus is well-capitalized and backed by top-tier VCs, including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized by LinkedIn as a 2025 Top Startup!

You will work on research and development of the Haus Causal MMM product. You will be an active contributor to the full product life cycle, collaborating with engineers, product managers, and designers to produce high-quality experiences for our customers.

The ideal candidate will be a hands-on economist/applied scientist with experience in the modern marketing measurement stack (incrementality testing, MMM, MTA) with excitement to both develop new methodologies and directly implement and deploy them via scalable software solutions. You thrive on execution and are able to use strong judgment and clear, succinct communication to drive the right decisions to deliver on our ambitious roadmap.

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  Partner with engineering teams to build and deploy causal models into production at scale

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  Leverage existing literature and develop new science in the causal marketing measurement space

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  Help drive product roadmap by clearly advocating for rigorous scientific solutions to customer pain points

• MSc/PhD in Economics or equivalent industry/academic experience

• 4+ years working in an Economist / Data Scientist / Applied Scientist role building science models for production environments

• Expert in Python and SQL

• Experience in the modern marketing measurement stack (incrementality testing, MMM, MTA)

• Experience in causal inference and machine learning

• Experience coding and troubleshooting models built for deployment
  
  

• Aim high - you seek to expand on the existing scientific literature by developing novel methods of answering core customer questions

• Experiment boldly - you try new approaches and iterate quickly on new scientific hypotheses

• Done is better than perfect - you take small exploratory steps rather than large precise leaps toward solutions.

• Act like an owner - you share responsibility with the team and do what you can to achieve success. You thrive in ambiguity and find ways to structure unstructured problems.