Senior Economist / Applied Scientist

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

As a Senior Economist / Applied Scientist, you will own building models and collaborating with engineers to effectively integrate customer data, deploying models that consistently deliver high-quality results for a rapidly expanding base of enterprise clients. You will lead the research and development of the GeoLift model, our core offering, focusing on feature enhancements that ensure accuracy, scalability, and significant business impact. By driving experimentation and A/B testing methodologies, you will facilitate rigorous statistical evaluations of business outcomes. Additionally, you will provide scientific leadership, fostering a robust knowledge base in causal modeling while building and mentoring a growing team of scientists. Staying attuned to industry trends and emerging technologies, you will recommend innovative tools and approaches to elevate our capabilities.

Please apply if you are passionate about solving complex problems, eager to collaborate with cross-functional teams, and ready to make a tangible impact as a leader in a growing science organization.

Responsibilities

• Partner closely with our Customer Success team and Haus science and product leads to answer questions, identify gaps and pain points, and find opportunities to improve our products.
• Develop and implement scalable solutions to enable our customers to clearly and easily interpret results, gain insights, and drive business decision making.
• Own building solutions, models, and products, while working cross-functionally with scientists, designers, and engineers to ingest customer data and deploy models and tools that deliver meaningful 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.

Qualifications

• MSc/PhD in Economics, Statistics, Quantitative Marketing, or equivalent industry/academic experience
• 5+ years working in an Data Scientist role building science models/products for production environments
• Proven experience in causal inference, statistics, and machine learning
• Demonstrated ability to work with customers and/or cross functional stakeholders
• 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 startup equity
• Top of the line health, dental, and vision insurance
• 401k plan
• 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.