Teams at Superhuman

Analyze acquisition data and guide marketing strategy, budget allocation, channel optimization, and growth decisions. Design incrementality experiments, develop marketing mix and allocation models, define acquisition metrics, identify new growth opportunities, and improve web conversion. Partner with Marketing, Engineering, Finance, Design, and User Research to translate statistical and machine learning insights into measurable business outcomes across paid, organic, consumer, and enterprise channels.

Build and maintain Databricks data pipelines and reusable models for Go product analytics. Define metrics, improve event logging, connect product usage with customer data, and support adoption and growth analysis. Ensure data quality through testing, monitoring, troubleshooting, and documentation. Optimize pipeline performance and costs while collaborating with Product, Engineering, and Data Science. Contribute dashboards and analyses that support product and business decisions.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.