Data Scientist

Summary

Join Haus, a company revolutionizing digital marketing with its causal inference platform, and contribute to shaping the future of media and advertising. As a Data Scientist, you will be responsible for asking and answering crucial marketing questions, designing and implementing analytical frameworks, and delivering impactful insights. You will leverage advanced machine learning and causal inference techniques to analyze large datasets and communicate findings to both technical and non-technical audiences. This role requires a strong background in data science, econometrics, and causal inference, along with excellent communication and collaboration skills. Haus offers a competitive salary, comprehensive benefits, and a collaborative work environment.

Requirements

• 3+ years of experience in a Data Scientist role
• Proven track record of building and deploying data science or econometric models in production environments
• Expertise in Causal Inference and Machine Learning
• 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 to drive alignment and deliver measurable results
• Ability to explain complex ideas to non-technical audiences and translate business needs into technical solutions

Responsibilities

• Ask and answer important business, product, and research questions related to media and advertising
• Respond to customer questions and partner with them on solutions
• Develop robust economic and statistical frameworks to address causal inference and marketing measurement questions
• Leverage state-of-the-art methodologies to test hypotheses, validate findings, and inform strategic decisions
• Translate complex theoretical approaches into practical, scalable production models
• Conduct in-depth analyses of large datasets, combining advanced machine learning with causal inference to surface actionable insights
• Communicate conclusions clearly and persuasively to 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 on causal inference in media and advertising

Preferred Qualifications

• Experience in adtech or related domains (e.g., marketing measurement, media optimization)
• 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

Benefits

• 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)
• $160,000 - $180,000 a year