Analytics and Data Science Lead

Scapia; Bengaluru; On-site; INR 4,200,000 - 6,600,000; Full time

Job summary

Scapia in Bengaluru seeks a senior analytics leader to own and scale the LTV charter across the Card and App. You will partner with product, growth, marketing, risk, and business teams to drive activation, engagement, retention, and monetization through data-driven decisions.

You will build segmentation, run lifecycle interventions, and mentor a growing analytics team, leveraging AI tools for faster, scalable insights and impact.

Qualifications

• 7–10 years of experience in analytics/data science or a closely related quantitative field.

Responsibilities

• Own the LTV charter for Scapia customers: activation, engagement, retention, and monetization, partnering with product, growth, marketing, business, risk, and other teams.
• Influence decisions on the Card's CVP and its impact on customer experience and P&L.
• Drive growth in customer spends and MAU as core charter metrics.
• Design lifecycle interventions to induce spend and build activation/retention programs for the Scapia App.
• Build and mentor an analytics/data science team as the function scales and use AI tools to accelerate output.
• Conduct competitive wallet-share analyses and continuous segmentation to enable targeted interventions.

Skills

Advanced SQL

Python

R

A/B testing

LTV

Data storytelling

Stakeholder management

Leadership

AI tools

Experimentation

Tools

dbt

Airflow

Snowflake

BigQuery

Redshift

Job description

• 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
• behavior
• 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
• Identify attriters and the early signals that predict attrition
• Design and run retention interventions to win these users back before they churn
• Explore milestone structures beyond a single threshold (e.g., ₹20K for annual percentage/rewards) — monthly vs. annual milestones, tiered targets, etc.
• 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
• 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
• Gaining traffic to the App, improving opportunities for crossconversion and MVUSs
• Providing assortment optimisation for App customers and driving bi-annual Castline reviews
• Supporting US customer engagement program and success measurements to reduce churn rates
• Reducing cost to serve through dynamic customer scoring and automated service design optimized customer journey recommendations, fraud detection
• Running utilisation and forecast modelling of over 70 customer segments across LTV events and conversion behaviour
• Tracking ROI and monitoring any arising leakage, new customer attraction, or segregation impacts

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