About Us Paytm is India’s leading payments Super App, offering consumers and merchants a wide range of financial services. A pioneer of the mobile QR payments revolution, Paytm’s mission is to bring half a billion Indians into the mainstream economy through technology-led financial inclusion. Paytm Money is the wealth-tech arm of Paytm, enabling users to invest in mutual funds, equities, and derivatives seamlessly. Owned by One97 Communications, founded by Vijay Shekhar Sharma, the company is headquartered in Noida and backed by leading global investors. Role Overview We are looking for a strategic, business-oriented, and AI-forward Analytics Leader to drive data-led and AI-powered decision-making for Paytm Money across equities, mutual funds, and derivatives. This role will define and lead the analytics vision across user growth, trading behavior, portfolio analytics, customer lifecycle, monetization, and product experience, while partnering closely with Product, Growth, Business, Risk, and Engineering teams to scale Paytm Money’s wealth platform. The ideal candidate will bring deep expertise in financial markets, broking platforms, and investment products, along with strong experience in building scalable analytics functions. In addition, this leader should have worked intensively on AI-led modeling, predictive decision systems, AI adoption across analytics workflows, and data models designed for advanced analytics and machine learning use cases. This is not just a reporting or dashboarding role. It is a leadership role focused on building a proactive, predictive, and AI-enabled analytics engine that helps the business make faster, sharper, and more scalable decisions. Key Responsibilities 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. Key KRAs / Success Metrics · Improvement in conversion rates across onboarding, KYC, activation, and trading / investing funnels · Growth in active traders, active investors, AUM, and repeat participation · Increase in trading frequency, engagement depth, and customer retention · Improvement in CAC, LTV, ARPU, and overall unit economics · Accuracy, adoption, and business impact of predictive models and AI-driven recommendations · Faster and higher-quality decision-making across Produ