Analytics and Data Science Lead

• Location: Bangalore

• Experience: 7–10 years in analytics / data science

• Mandate: Own and grow Customer Lifetime Value across the Scapia Card and App

Scope of the Role

• Own the LTV charter for Scapia customers: activation, engagement, retention, and monetization. Partner with product,

growth, marketing, business, category, risk team to bring intelligence in decision making, accuracy in measurement and

uplift impact on co-owned metrics

• Influence decisions on the Card's Customer Value Proposition (CVP), and its downstream impact on both customer

experience and P&L

• Drive growth in customer spends and Monthly Active Users as core charter metrics

• Identify how to convert regular users into super users, and the levers that drive that transition

• Define customer segments, optimal interventions, and the journey map to move users up the value ladder

• Design and run lifecycle-based interventions to induce spend, in partnership with the Cards Business team

• Build activation, engagement, and retention programs for the Scapia App specifically (not just the card)

• Build and maintain a view of competitive share of wallet, and how Scapia can grow its share of customer spend

• Start with goal-specific customer segmentations across LTV initiatives; over time, identify the opportunity to unify these

into a single, company-wide segmentation that simplifies interventions, tracking, and customer solutions

• Build and mentor an analytics / data science team as the function scales

• Use AI tools throughout the workflow — for analysis, modeling, coding, and reporting — to move faster and scale the team's output.

Example Projects You'll Drive :

• First-transaction program: get new customers to their first transaction while minimizing cannibalization of organic

behavior

• Power-user conversion program:

◦ Define what a power user is and identify early indicators that predict who becomes one

◦ Design a rewards journey to nudge users toward power-user behaviors (e.g., experiencing a 2% rewards

transaction, adding a second card, using UPI)

◦ Minimize cannibalization while designing these incentives

• Card attrition program:

◦ Identify attriters and the early signals that predict attrition

◦ Design and run retention interventions to win these users back before they churn

• Milestone / gamification design:

◦ Explore milestone structures beyond a single threshold (e.g., ₹20K for annual percentage/rewards) — monthly vs. annual milestones, tiered targets, etc.

◦ Test fear (loss-aversion) framing vs. greed (reward) framing

• Competitive wallet-share analysis: understand what else lives in the customer's wallet, and how Scapia can win a larger share.

• Rewards awareness campaigns: build, run, and measure programs (e.g., 2% everywhere, everyday card) jointly with the Cards Business team

• App activation modeling: for new/cold-start users (≤45 days), predict which onboarding module or homepage widget

each customer is most likely to convert on, using onboarding signals, card spend, and travel-affinity predictions etc.

• App engagement modeling: for repeat users (45+ days), personalize category order, homepage composition, and

widget/collection ranking using lifecycle signals (last click, last purchase, last travel), intent, and session-level history — including building a real-time category affinity score per customer

• In-app recommendations: power contextual prompts like because you searched, use your coins, or best fit for your budget using property-specific signals

• Next-best-category / next-best-action modeling: drive continued exploration and conversion after a customer's first transaction, based on category-level propensity

• App churn and retention modeling: shift focus from growth to retention as usage signals decline, using inactivity and usage-pattern data to predict churn propensity

What We're Looking For

• 7–10 years of experience in analytics, data science, or a closely related quantitative field, with demonstrated readiness to

own a strategic charter rather than execute a defined roadmap. Designs team according to the charter and impact.

• Track record of driving measurable business outcomes (LTV, retention, activation) — not just reporting

• Experience designing and analyzing experiments (A/B tests, uplift models) and distinguishing correlation from causal impact

◦ Grounded in an analytical approach, with a strong nose for where the real value lies

◦ Led by impact and execution, not just analysis for its own sake

◦ Has a keen eye for data-led measurement of experiments, with test designs shaped by sample size, bias, and contamination considerations

• Background in consumer fintech, credit cards, subscription businesses, e-commerce, or another domain where LTV and retention economics are core to the business

• Experience partnering directly with Product, Growth, Marketing, Risk, and Business teams to embed data science into the roadmap — and influencing decisions at the leadership level

• Builds with peers through alignment and collaboration

• Manages senior stakeholders well, through crisp, impact-led communication

• Comfort operating with ambiguity in a fast-moving startup environment, and building structure where none exists yet

• Brings coherence across different projects rather than letting them run as disconnected workstreams

• Strong SQL skills and hands-on experience with Python/R for building predictive models (churn, LTV, propensity,

next-best-action)

• Fluency with AI tools (e.g., coding copilots, LLM-based analysis/automation) as part of everyday working style, not just as a side skill.

Nice to Have :

• Prior experience leading or mentoring analytics/data science teams, including building a function or team from scratch

• Familiarity with modern data stacks (dbt, Airflow, Snowflake/BigQuery/Redshift, or similar)

• Experience with experimentation/feature-flagging platforms

• Exposure to rewards/loyalty program design or gamification mechanics