Staff Data Analyst

About Our Client

This organization operates in the FinTech sector, addressing economic inclusion and financial resilience. It focuses on providing accessible tools to help individuals build credit and savings, targeting over 100 million Americans with no or low credit. The organization emphasizes collaboration to ensure its products reach those who can benefit most.

About the Opportunity

The Staff Data Analyst role within the Product and Customer Analytics team is focused on driving sustainable growth across the customer lifecycle. This position involves owning key metrics and analytical systems related to credit products and customer engagement. The role requires applying deep analytical skills to influence business decisions and collaborating across teams to enhance data-driven strategies.

Responsibilities

• Lead analytical projects to identify drivers of customer and product behavior beyond correlations.
• Collaborate with Data and Analytics Engineering on data model development.
• Partner with Product, Marketing, and Engineering teams to define measurement strategies and influence the business roadmap.
• Develop and manage AI-driven tools and semantic workflows to optimize analytics processes.
• Maintain business intelligence dashboards and reporting on lifecycle marketing KPIs and campaign performance.
• Apply advanced analytical techniques including statistical modeling, predictive analytics, and causal inference.
• Communicate insights effectively to technical and non-technical stakeholders, including senior leadership.
• Stay informed on trends in financial products, AI, analytics engineering, and business intelligence.

Requirements

• Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, or related fields.
• Over 8 years of experience in data analytics supporting product teams, preferably in fintech or related consumer industries.
• Expertise in SQL within cloud-based data warehouses such as Snowflake, Redshift, or BigQuery.
• Experience with BI tools like Tableau, Looker, or Power BI.
• Proficiency with analytics engineering ELT tools such as dbt, Databricks, Airflow, or Git.
• Experience working with AI tools including Claude or Codex.
• Strong skills in programming languages for data analysis, such as Python or R.
• Knowledge of A/B testing and causal inference methods, including Bayesian and Frequentist approaches.
• Strong analytical, problem-solving, critical thinking, and communication skills.
• Ability to translate business questions into actionable insights for diverse audiences.