Data Scientist

Responsibilities

• Build dashboards and data sources that enable self-serve – clear, trusted views that drive operating cadence and day-to-day decisions.
• Insights discovery – establish a rhythm that consistently surfaces what matters to the people who need it most.
• Own metric quality with stakeholders – partner with Product and Engineering to ensure we can answer the most important questions and ensure reliability.

Requirements

• Structured approach to problem solving and the ability to turn fuzzy questions into crisp analysis and clear actions.
• Strong bias for action and comfort owning end-to-end analyses, from problem framing to data pulls, insights, recommendations, and follow-through.
• Excellent communication skills and experience acting as a cross-functional thought partner to both technical and non-technical audiences.
• Advanced SQL skills, including comfort with complex joins, edge cases, and messy schemas.
• Advanced experience with BI tools (Metabase, Mode, Lightdash, Hex, or similar) and designing dashboards for self-serve analytics.
• 5–8+ years of experience in data analytics, business analytics, data science, product analytics, consulting, or related roles.

Nice-to-Haves

• Experience in fintech, payments, card programs, or financial infrastructure.
• Python proficiency for analysis/automation (pandas, notebooks, lightweight pipelines).
• Strong spreadsheet skills; comfort building and QA’ing metric logic.
• Experience using LLM tools to accelerate analysis (e.g., classification of tickets, summarization, automated insight generation).
• Familiarity with modern data stack concepts (dbt, dimensional modeling, metrics layers) even if you’re not the primary owner.