Data Scientist - Customer Facing

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

You will partner directly with Customer Success leaders to scale the delivery and interpretation of results from our cutting-edge science platform to meet fast growing customer demands. You will serve as a trusted advisor to our customers, focusing on driving value and confidence in results. Collaborating across Customer Success, Science, Engineering, and Product teams, you will develop solutions to ensure robust, reliable results and seamless communication at scale.

The ideal candidate is a seasoned analyst who is comfortable diving deep into data analysis and communicating actionable insights to diverse audiences. You will be instrumental in developing solutions that drive customer value and establish best practices for results robustness, process efficiency, and technical clarity. You thrive in dynamic environments, enjoy working with cross-functional stakeholders, and are passionate about continually improving processes. As a senior contributor, you will lead by example, mentoring junior analytical leads, establishing best practices, and fostering a culture of technical excellence and collaboration.

Please apply if you want to provide real value, learn new things, and own critical components of a growing data science product.

Responsibilities

• Partner closely with our Customer Success, Science, and Product leads to identify gaps and pain points in our modeling and results delivery process.
• Serve as a trusted advisor to our customers on interpretation of test results and scientific methodology, distilling complex concepts into actionable recommendations.
• Develop and implement solutions to up-level existing workflows, visualizations, metrics to bring high-quality insights at scale to all of our customers.
• Act as a mentor and resource for junior analytical leads, sharing technical expertise in statistical/econometric concepts and best practices like tests, validations, monitoring, and alerting.

Qualifications

• Degree in Economics, Statistics, Data Science, or related field.
• 5+ years working in a Data Scientist/Analyst role.
• Strong communication skills with the ability to synthesize complex technical concepts concisely and precisely.
• Proven ability to present to customers and/or cross-functional stakeholders.
• Experience coding and troubleshooting statistical models.
• Experience working with Python and SQL.
• Experience with marketing measurement (specifically regional testing) is preferred

About you

• Done is better than perfect - you take small exploratory steps rather than large precise leaps toward solutions.
• Act like an owner - you take shared responsibility for team success. You bring attention-to-detail and rigor to model and data deep dives.
• Be curious - you’re eager to test new ideas and solve novel problems.

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.