Data Scientist - Marketing Science

About Haus

For the past 20 years, digital marketing has used your data without consent. This won’t be the case moving forward. With increasing consumer privacy, brands will need to rely on new tools to grow efficiently. Our causal inference platform gives customers the tooling they need to understand what drives their business. Whether advertising, promotions or emails, Haus helps our customers align their investment – time, money and resources – to drive incremental business outcomes. 

Our team previously built these tools at industry leaders like Google, Amazon, Netflix, Lyft, and Spotify. Haus has strong customer traction and significant revenue from household name brands. Our customers rave about our solutions, and we are backed by top VCs like Baseline Ventures and Haystack. 

• Proactively identify key business, product, and research questions that will shape our understanding of media and advertising.
• Respond to questions from customers and partner with them on solutions. 

• Develop robust economic and statistical frameworks to tackle questions around causal inference and measurement of marketing.
• Leverage state-of-the-art methodologies to test hypotheses, validate findings, and inform strategic decisions.
• Translate complex theoretical approaches into practical models that can be deployed and scaled in production environments.

• Conduct in-depth analyses of large datasets, combining advanced machine learning techniques with causal inference frameworks to surface actionable insights
• Communicate conclusions clearly and persuasively to both technical and non-technical audiences, influencing product roadmaps and client strategies.

• Produce high-quality research documentation, technical specifications, and knowledge-sharing materials.
• Publish internal white papers or external thought leadership pieces when appropriate, illustrating the value of cutting-edge causal inference in media and advertising

• Proven track record of building and deploying data science or econometric models in production environments.
• Experience in adtech or related domains (e.g., marketing measurement, media optimization) is a significant advantage.

• Hands-on experience with experiment design (e.g., A/B testing, quasi-experimental designs) and advanced modeling.
• Familiarity with relevant frameworks (e.g., difference-in-differences, Bayesian methods, uplift modeling).

• Proficiency in Python.
• Comfort working within modern data pipelines (e.g., SQL, Spark, cloud environments).
• Ability to optimize, debug, and maintain production-level code.

• History of working closely with diverse teams—product managers, engineers, external stakeholders—to drive alignment and deliver measurable results.
• Comfortable explaining complex ideas to non-technical audiences and translating business needs into technical solutions.

• Keen interest in staying at the cutting edge of technology and analytics.
• Self-driven approach to problem-solving, with a willingness to define questions, lead workstreams, and see them through to impactful delivery.

• Done is better than perfect - you take small flawed 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.

• 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.