Economist / Applied Scientist

Mountain View, CA
Science /
/ Remote
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, Baseline Ventures, and Haystack.

What you'll do
You will work closely with our larger enterprise customers to understand their problems and develop valuable solutions. Collaborating with other scientists and our engineering team, you will develop these solutions into products on our platform that will benefit our growing customer base.

The ideal candidate is somebody who is both an effective economist/applied scientist and an excellent communicator, with an appreciation for the complexities of businesses and how they're run. You have the desire to work with customers and enjoy improving existing solutions as well as designing tailored solutions for customers. Please apply if you want to work with customers, provide real value, learn new things, and have a wide breadth of responsibility.


    • Partner closely with our enterprise customers and Haus science and product leads to identify gaps and pain points.
    • Develop and implement novel solutions to empower our customers to find answers to important business questions and help drive decision making. These solutions could develop into full products and expand Haus's product suite.
    • Own building solutions, models, and products, while collaborating with scientists and engineers to ingest customer data and deploy models and tools that deliver results on a regular cadence. Leverage best practices like tests, validations, monitoring, and alerting, to ensure high quality outputs and experiences for a growing number of enterprise customers.


    • MSc/PhD in Economics or equivalent industry/academic experience
    • 2+ years working in an Economist / Data Scientist / Applied Scientist role building science models for production environments
    • Experience in causal inference and machine learning
    • Experience coding and troubleshooting models built for deployment
    • Experience working with Python and SQL

About you

    • 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.
    • Experiment - you try new ideas rather than repeat known formulas.

What we offer

    • Competitive salary and early 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)
$150,000 - $180,000 a year
The salary range for this position is expected to be $150,000 - $180,000. Salary ranges are determined by role and level, and within the range individual pay is determined by additional factors including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in this job posting reflect the base salary only, and do not include equity or benefits.
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.