The Role
About Haus
Haus is a marketing science platform that helps brands measure and maximize the business impact of their marketing spend with scientific precision. Over $360B 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 re-allocate it to maximize growth.
Haus was built by a team of former product managers, economists, and engineers from Google, Netflix, Meta, and others to make high-quality decision science accessible to businesses of all sizes. By automating the heavy lifting of experiment design, data processing, and insights generation, we empower our customers to make more profitable, data-driven decisions. We hear our customers frequently rave about our product, for example "we've seen north of 10x ROI on our annual investment in Haus in the first 2 months alone.”
Haus is on a hypergrowth trajectory, 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 and has made the list for 2025's exceptional startups!
What you'll do:
This role will lead high-impact projects from the ground up. You’ll have the autonomy to design and build systems that could fundamentally reshape the advertising ecosystem. The work will involve developing complex intervention systems, advanced marketing planning tools, and optimization engines powered by machine learning and causal inference.
We’re looking for individuals who thrive on solving tough problems, think critically, and love turning ideas into scalable, production-ready systems. If you enjoy both writing production code and building innovative solutions, this role is for you.
Responsibilities:
- Drive initiatives from concept to final product delivery, ensuring seamless end-to-end execution: lead or contribute to the design, development, optimization, and product ionization of machine learning (ML) solutions for complex and high-impact problems.
- Implement probabilistic techniques into reusable statistical libraries, including bootstrapping, statistical tests, and ML models/regressions.
- Build and maintain the ML systems that power Haus’ product lines.
- Review code and designs of teammates, providing constructive feedback.
- Lead and collaborate with engineering and cross-functional partners across product, engineering, and science teams to drive system development from ideation to production.
Qualifications:
- BS or MS in Computer Science, Engineering, Mathematics or related field
- Must have3+ years of work experience in Adtech industry
- Must have 5+ years of working experience as a 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++).
Nice to have:
- Exposure to LLMs or AI Agentic field
- Experience in modern deep learning architectures and probabilistic modeling.
- Expertise in the design and architecture of ML systems and workflows.
- Experience Data Science or machine learning approaches in marketing and growth
What we offer:
- Competitive salary and startup equity
- Top of the line health, dental, and vision insurance
- 401k plan
- Provide you with the tools and resources you need to be productive (new laptop, equipment, you name it)
- Small team with big impact on the overall output
Haus is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.
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The Company
What We Do
Haus is a decision science platform built on your own data. Our products combine state-of-the-art causal inference and econometrics to help brands make informed investment decisions.
