Role summary:

Data is the heartbeat of our business. We need a hands-on analyst who can transform billions of transaction rows into activation playbooks, profit levers, and fraud-crushing insights. You’ll be the bridge between raw transaction logs and real-world decisions, working shoulder-to-shoulder with Business, Product, Growth, Risk and Finance. If turning noisy data into clear human actions is your idea of fun, keep reading.

Responsibilities:

Build the single source of truth

• Combine processor, ledger, and engagement data in Big Query/Snowflake

• Maintain reproducible SQL/Python jobs that refresh daily

• Publish core dashboards in Power BI or Grafana

• Automate a 10-slide board deck that ships on day 2 of every month

• Drive automation in reporting

Define and execute portfolio and financial analysis

• Define and conduct segmentation analysis on spending behavior and life-cycle stage

• Flag dormant or low-engagement cards and supply re-activation lists to CRM

• Slice CAC/LTV by cohort; re-allocate spending toward high-retention channels

• Develop cost-benefit analyses on new product/feature recommendations/changes

• Build volume forecasts and elasticity models that help us secure better network terms

• Provide Finance with scenario tables before every partner renegotiation

• Highlight cost variances or fee leakages as they occur

Spend-growth support

• Quantify the impact of campaigns, rewards, limit changes, and partner offers

• Identify merchant-category and time-of-month triggers that correlate with higher usage

• Advise Business Development on reward partners that fit observed spend patterns

Be the fraud loss assassin

• Map false-positive hotspots, tune rules, partner with ML team on new features

• Drive ≥25 bps annual reduction in fraud losses without hurting approval rates

Requirement:

Must-haves

• 3–6 yrs in card issuing, payments, lending, or top-tier analytics role

• Strong SQL; competent Python or R

• Experience with a modern BI tool (Looker, Power BI, Mode, etc.)

• Comfort with statistical testing, cohort and survival analysis

• Storytelling skills—turn noise into narratives executives act on

• Bias for delivering an initial solution quickly and iterating

• Bonus points for experience with fraud decision engines, interchange tables, or basic ML deployment—but curiosity beats checkboxes every time

Nice-to-haves

• Familiarity with interchange regulations and scheme fee tables

• Exposure to fraud rule tuning or decision-engine data

• Startup or scale-up DNA

Examples of how we’ll measure success

• Increase in active card rate

• Fraud and fee drag reduction

• Executive and commercial teams rely on the new dashboards as their primary source of portfolio data

• Number of insight-driven product launches / adaptations

• Reduction in Management and Board reporting time