Make smarter enterprise marketing investments with Haus’ AI-powered causal marketing platform and expert guidance. Backed by Insight.
About the role
This role will drive high-impact projects for advanced marketing planning, analysis, and optimization at Haus using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, turning ideas to scalable systems. This role specifically will work deeply on the cMMM machine learning problem space.
What they're looking for
- PhD or equivalent experience in Computer Science, Engineering, Mathematics or related field
- 10+ years of industry experience ideally with a focus on Machine Learning Engineer, building and operating production ML systems
- Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design
- Experience working with cross-functional teams (product, science, product ops etc)
- Proficiency in one or more object-oriented programming languages (e.g. Python, Go, Java, C++)
More about this role
Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide. With support from PhD economists, data scientists, and growth experts, Haus’ AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands like Dyson, Wayfair, Sonos, Fanduel, SharkNinja, and Intuit to optimize spend, accelerate growth, and make smarter marketing decisions at scale.
This role will drive high-impact projects for advanced marketing planning, analysis, and optimization at Haus using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, turning ideas to scalable systems. This role specifically will work deeply on the cMMM machine learning problem space. The role will be a blend of working with applied scientists, data scientists, data engineers and other MLEs to deliver trustworthy results to our customers while focusing on creating processes that help scale the business.
Drive initiatives from concept to final product delivery, ensuring seamless end-to-end...
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