Data Scientist
About the Role
You will analyze growth funnels, campaign performance, and user cohorts to surface acquisition and retention drivers and inform product decisions. You will design, build, and maintain scalable data pipelines and structured models in the data warehouse to power marketing dashboards and self-serve analytics. You will build pipelines for blockchain transactions, order book, and market participant data and ensure high data quality via validation, monitoring, and documentation. You will design and evaluate A/B tests, own attribution analyses, and present clear visualizations and KPIs to influence marketing and leadership.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Statistics, Economics, or a related quantitative field
- 4+ years of experience in data science, analytics, or product analyst roles
- 2+ years of experience working with blockchain or order book data
- Expertise in SQL and database technologies
- Experience with cloud databases and reporting technologies such as BigQuery, GCP, and Mode
- Proficiency in Python
- Experience with data visualization tools
- Solid understanding of ETL processes, data modeling, and data warehousing
- Entrepreneurial mindset and intellectual curiosity with strong analytical hypothesis development
Responsibilities
- Collaborate with marketing and cross-functional teams to translate campaign strategies and product metrics into actionable insights using SQL and Mode
- Analyze growth funnels, campaign performance, and user cohorts to identify acquisition and retention drivers
- Own attribution and cohort analyses across marketing channels to optimize user acquisition
- Design and evaluate A/B tests and lifecycle marketing strategies to improve conversion and engagement
- Design and maintain scalable data pipelines and structured models in the data warehouse
- Build pipelines for blockchain transaction, order book, and market participant data
- Own and evolve marketing dashboards with KPIs and clear data visualizations to inform leadership
- Implement validation, monitoring, and documentation to ensure data quality and governance
- Provide guidance to teammates on how to use and interpret data
- Continuously enhance data infrastructure by optimizing systems and evaluating new tools