About Haus
Haus is a first of its kind decision science platform for the new digital privacy paradigm where data sharing and PII is restricted. Haus uses frontier causal inference based econometric models to run experiments and help brands understand how the actions they take in marketing, pricing and promotions impact the bottom line. Our team is comprised of former product managers, economists and engineers from Google, Netflix, Amazon and Meta who saw how costly it is to support high-quality decision science tooling and incrementality testing. Our mission is to make this technology available to all businesses, where all the heavy lifting of experiment design, data cleaning, and analysis/insights are taken care of for you. Haus is working with well known brands like FanDuel, Sonos, and Hims & Hers, and has seen more than 30x ROI by running experiments and helping brands make more profitable decisions. We are backed by top VCs like Insight Partners, 01 Advisors, Baseline Ventures, and Haystack.
What you'll do
This is a unique opportunity to help us build robust and scalable systems that handle ML and Science workloads at Haus. The ML/Science Platform is at the intersection of product, data, and science workflows; and connects the major systems that power the Haus product.
The ideal candidate is an accomplished engineering leader and an excellent communicator, who has deep experience with scalable distributed systems, and is well-versed with data and machine learning systems/workflows. Please apply if you are a great technologist who enjoys leading from the front, learning new things, and a wide breadth of responsibility.
Responsibilities
- Drive engineering execution within the Science Platform team on a regular basis
- Craft, communicate, and execute on the team’s vision and roadmap in partnership with our platform PM
- Collaborate with science research, infrastructure, and data platform engineering to make sound, first-principle decisions
- Partner with product engineering squads to ensure high quality, efficient delivery of model outputs into production
- Work closely with scientists and MLEs to create an ergonomic workflow as a paved path that maintains some flexibility
- Act as a strong summarizer that is able to make tradeoffs among priorities to ensure consistent, high-quality delivery
Qualifications
- 5+ years of experience managing a team of engineers delivering data and ML products
- 10+ years of experience as a software or machine learning engineer
- Demonstrated experience building ML products from 0 -> 1
- Strong execution skills to keep work on track
- A healthy balance between digging into code/architecture and leading through others
- A bias towards quantitative measures of progress and performance
- Demonstrated experience managing and growing engineers up to and through the staff level
Bonus points
- Experienced with ML workflows in a cloud computing environment
- Earlier stage startup experience
- BS/MS/PhD in Computer Science, Applied Mathematics or a related field
What we offer
- Competitive salary and early startup equity
- Top of the line health, dental, and vision insurance
- 401k plan
- Unlimited PTO with a 10 day minimum
- Provide you with the tools and resources you need to be productive (new laptop, equipment, you name it)
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.
Top Skills
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.
