1. Analytics Strategy, AI Vision & Business Impact
· Define and own the analytics and decision intelligence roadmap aligned with Paytm Money’s growth, engagement, and revenue goals.
· Translate business problems across acquisition, activation, trading behavior, retention, and monetization into structured analytical and AI-led problem-solving frameworks.
· Drive a data-first and AI-enabled decision culture across Product, Growth, Business, Marketing, Risk, and Finance teams.
· Evolve the analytics function from descriptive reporting to predictive and prescriptive decision support.
· Identify high-impact use cases where AI/ML can materially improve conversion, retention, customer engagement, monetization, or operational efficiency.
2. Investment, Broking & Customer Lifecycle Analytics
· Analyze user behavior across equities, derivatives, and mutual funds journeys spanning onboarding, KYC, activation, investing/trading, engagement, and repeat usage.
· Build deep insights on trading frequency, portfolio behavior, SIP trends, churn, investor segmentation, and cohort performance.
· Develop analytical frameworks for order flow, liquidity behavior, margin usage, derivatives participation, and investment lifecycle progression.
· Use behavioral, transactional, and product data to identify customer patterns and opportunities for improved engagement, advisory, and cross-sell.
3. Growth, Funnel & Monetization Analytics
· Drive end-to-end funnel analytics across acquisition, onboarding, activation, engagement, and retention.
· Build and optimize core business models such as CAC, LTV, cohort retention, attribution, and profitability measurement.
· Identify and prioritize drop-offs across key journeys such as demat account opening, KYC completion, first trade, first SIP, and repeat investing.
· Support monetization strategy across brokerage, commissions, margin products, subscriptions, and cross-sell opportunities through data-led insights and experimentation.
4. AI-Driven Analytics, Predictive Modeling & Decision Systems
· Lead the development and application of advanced analytics and AI/ML models such as churn prediction, conversion propensity, trading propensity, LTV forecasting, cohort scoring, anomaly detection, and recommendation models.
· Drive the use of AI for proactive opportunity identification, risk signaling, growth optimization, and personalized user experiences.
· Work on AI-led decision systems that enable next-best-action recommendations, customer targeting, funnel prioritization, and personalization at scale.
· Ensure model outputs are translated into business actions, product interventions, and measurable outcomes rather than remaining isolated analytical exercises.
· Bring hands-on understanding of model adoption, performance tracking, and business trust in AI-driven outputs.
5. AI Adoption Across Analytics Workflows
· Drive adoption of AI-enabled analytics workflows across teams, including automated insight generation, intelligent reporting, decision-support tooling, and scalable self-serve analytics.
· Explore and enable use cases where GenAI and AI copilots can improve insight discovery, data interpretation, operational speed, and leadership reporting.
· Help business and product stakeholders consume advanced analytics outputs in a simple, actionable, and decision-friendly manner.
· Build organizational confidence in using AI not as a side capability, but as a core lever for better business execution.
6. Product, Experience & Personalization Analytics
· Partner with Product teams to improve user journeys, onboarding experiences, conversion funnels, and feature adoption.
· Provide insights to enhance UI/UX, customer segmentation, personalization, nudges, and recommendation engines.
· Support real-time and near-real-time analytics use cases that improve responsiveness of product and growth interventions.
· Collaborate with teams to identify where AI-led personalization and intelligent nudging can improve activation, retention, and monetization outcomes.
7. Experimentation, Measurement & Causal Learning
· Build and institutionalize robust experimentation frameworks including A/B testing, holdout design, cohort analysis, and incrementality measurement.
· Ensure that product, growth, and monetization decisions are backed by rigorous measurement and statistically sound evaluation.
· Create closed-loop learning systems where experiments, predictive models, and business actions continuously inform one another.
8. Data Modeling, Foundations & Analytical Readiness
· Partner with Data Engineering and platform teams to build robust data pipelines, warehousing, semantic layers, and reporting systems.
· Design data models that serve not only BI and reporting use cases, but also AI/ML, feature engineering, experimentation, and personalization use cases.
· Define reusable business metrics, event taxonomies, customer states, and analytical layers that improve consistency across dashboards, models, and decision systems.
· Ensure data accuracy, governance, traceability, and regulatory compliance, especially in a financial services environment.
9. Stakeholder Management & Team Leadership
· Act as a strategic thought partner to leadership by influencing decisions through data-backed and AI-informed recommendations.
· Collaborate closely across Growth, Product, Marketing, Risk, Finance, Data Engineering, and Data Science / ML teams.
· Build and lead a high-performing analytics organization comprising analysts, data scientists, BI engineers, and decisioning talent as needed.
· Mentor teams to raise the bar on problem structuring, business storytelling, technical rigor, and AI-first analytical thinking.