Staff Data Analyst

Self Financial•Austin, TX

6d•Hybrid

About The Position

Self Financial is a venture-backed, high-growth FinTech company with a mission to increase economic inclusion and financial resilience by empowering people to build credit and build savings. We're looking for people who share our passion and are driven to tackle challenges, find solutions and make the financial space better for the communities we serve. Our team is passionate about challenging the status quo of the credit industry by providing people accessible tools to take control of their credit. Executing on our mission requires deep collaboration across our teams to ensure our products reach the people who can benefit from them the most, particularly the 100 million+ Americans who have no or low credit. Role Summary: Self is looking for a Staff Analyst to join our Product and Customer Analytics team for our most important problem, driving sustainable growth throughout the customer’s lifecycle. This role is for a builder who works backwards from having a deep understanding of how the business functions, what data it takes to build that understanding, and then applying a deep analytical lens. You will have company-wide visible ownership of the metrics and analytical systems in the Credit Builder Account and Credit Card portfolio as well as the Customer lifecycle itself. You must be comfortable influencing teams and having a strong point-of-view that is loosely held but informed by data. You are joining a highly AI Native team with high autonomy across data modeling, analysis, and predictive use cases. You are expected to be comfortable with semantic layer build outs, and the core tenets of what it takes to build an AI native analytics platform that enables high velocity with high trust decision making.

Requirements

Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, or a related discipline.
8+ years of experience in a data analytics role, with significant experience supporting product teams, preferably within fintech or a related consumer-facing industry (e.g., banking, credit cards, lending).
Experience working in cloud-based data warehouses with proven expertise in SQL (e.g., Snowflake, Redshift, BigQuery).
Experience building dashboards and reports using BI tools (e.g., Tableau, Looker, Power BI).
Experience working with analytics engineering ELT tools (e.g., dbt, Databricks, Airflow, Git)
Experience with AI (e.g., Claude, Codex), including building skills, agents, and context harnesses.
Strong proficiency in at least one programming language for data analysis (e.g., Python, R).
Experience with A/B testing and causal inference, including both Bayesian and Frequentist approaches and quasi-experimental methods (difference-in-differences, etc.) for cases where a clean test isn't an option.
Excellent analytical, problem-solving, critical thinking and communication skills.
Ability to translate business questions into actionable insights and clearly explain technical concepts to non-technical audiences.

Nice To Haves

Familiarity with marketing automation platforms and customer data platforms.
Experience with predictive modeling techniques (e.g., regression, classification) and machine learning concepts.
Understanding of customer lifetime value (CLTV) modeling and cohort analysis.
Experience working in an agile development environment.

Responsibilities

Lead analytical projects/experiments that get past correlation to the actual drivers of customer and product behavior, segmentation, and engagement. Understanding which movements are noise versus those we should act on.
Contribute directly to the data models this portfolio runs on, in partnership with Data Engineering and Analytics Engineering.
Partner closely with Product, Marketing, and Engineering teams to define measurement strategies, set goals, and influence the business roadmap.
Build and manage agentic AI (Claude), domain context layers, and semantic workflows (dbt Metric Flow, Semantic Layer) to enable working smarter and automation of day-to-day functions.
Develop and maintain business intelligence dashboards and reporting that provide visibility into key lifecycle marketing KPIs, customer cohorts, and campaign performance.
Utilize advanced analytical techniques (e.g., statistical modeling, predictive analytics, causal inference) to uncover insights and forecast the impact of initiatives.
Communicate findings and recommendations effectively to both technical and non-technical audiences, including senior leadership.
Stay abreast of industry trends in financial products, AI, analytics, analytics engineering, and business intelligence.