The RoleThis 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.
What you'll do- 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.
- Able to 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 (specifically cMMM).
- 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.
- Drive design and implementation of AI (Agentic) workflows for ML pipelines (including model validation)
- Mentor ML engineers and raise the organization's ML bar
Qualifications- 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++).
Bonus Points- Experience in modern deep learning architectures and probabilistic modeling.
- Expertise in the design and architecture of ML systems and workflows.
- Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, and multi-armed bandits.
- Experience with MLFlow
- Experience with data science or machine learning approaches in marketing and growth
What We Offer:We're a high-performance, low-ego team operating in a fast-moving environment. We care deeply about our customers and expect everyone to take full ownership of their work - this is a place where high expectations fuel even higher growth.
If you thrive in ambiguity, take pride in raising the bar, and want to work alongside top-tier peers who challenge and support you, you'll find unmatched opportunities here. If you're looking for predictability or rigid structure or you prefer order-taking to go-getting, we're probably not the right fit - and that's okay.
We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape.
Some of our benefits include:
- Flexible PTO - take time when you need it!
- Equity - Startup environment with part-ownership in our successes
- Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best
- WFH stipend to support the set up you need to be productive
- Events & Offsites - opportunities to connect and celebrate in real life!
- Free Lunch - Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle)
- New Parent Leave - take time to welcome your newest Hausmate
We value in-person collaboration at Haus and give preference to candidates within commuting distance of our offices in San Francisco, Seattle, and New York City.
