Haus is a decision science platform focused on the new digital privacy paradigm where data sharing and PII is restricted.
The company utilizes causal inference based econometric models to run experiments that help brands understand the impact of their marketing, pricing, and promotions on their bottom line.
The team consists of former product managers, economists, and engineers from major companies like Google, Netflix, Amazon, and Meta.
Haus aims to make high-quality decision science tooling and incrementality testing accessible to all businesses.
The company has worked with well-known brands such as FanDuel, Sonos, and Hims & Hers, achieving over 30x ROI through their experiments.
The Data Scientist will work on research and development with the Media Optimization team to build next-generation causal marketing measurement tools.
Responsibilities include partnering with engineering teams to deploy causal models, applying novel models to new customers, advocating for scientific solutions to customer pain points, and supporting customers post-launch.
Requirements:
A Master's degree in a quantitative field such as Statistics, Economics, or Data Science, or equivalent industry/academic experience is required.
A minimum of 3 years of experience in an Economist, Data Scientist, or Applied Scientist role, generating insights using causal models and building science models for production environments is necessary.
Proficiency in Python and SQL is essential.
Experience with the modern marketing measurement stack, including incrementality testing, MMM, and MTA is required.
Knowledge of causal inference and machine learning is necessary.
Experience in coding and troubleshooting models built for deployment is required.
Benefits:
The position offers a competitive salary and startup equity.
Employees receive top-of-the-line health, dental, and vision insurance.
A 401k plan is provided.
The company supplies the tools and resources needed for productivity, including a new laptop and equipment.
