Head Analytics(GM/ AVP) , Paytm Money

Paytm

Noida, Uttar Pradesh, IndiaPosted 4 months ago

Skill Required

AnalyticsMachine LearningStatisticsEngineeringanalyticalbuildingTestNGdesignGenerative AIAI

Key highlights

12–15+ years of experience required in analytics, decision science, or data science with fintech/broking/wealth-tech exposure
Must have expertise in SQL, Python/R, BI tools (Tableau, Power BI), and modern data warehousing/transformation ecosystems
Unique opportunity to shape data, AI, and customer intelligence for the next generation of wealth products at scale
Key KRA: Accuracy, adoption, and business impact of predictive models and AI-driven recommendations

Role overview

Paytm Money, the wealth-tech arm of Paytm, is seeking a strategic, business-oriented, and AI-forward Analytics Leader to drive data-led and AI-powered decision-making across equities, mutual funds, and derivatives. This leadership role focuses on building a proactive, predictive, and AI-enabled analytics engine — not just reporting or dashboarding — partnering closely with Product, Growth, Business, Risk, and Engineering teams to scale the wealth platform.

Responsibilities

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
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
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
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
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
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
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
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
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

Requirements

12–15+ years of experience in analytics, decision science, or data science, with significant exposure to fintech, broking, wealth-tech, or investment platforms
Strong understanding of equities, derivatives (F&O), mutual funds, and digital investing / trading ecosystems
Proven experience building and scaling analytics functions that drive measurable business impact
Demonstrated experience in applying AI/ML techniques to solve business problems across growth, lifecycle, engagement, monetization, or risk
Strong exposure to predictive modeling, experimentation, customer segmentation, personalization, and data-driven product decisioning
Experience driving adoption of AI-led analytics in real business workflows, not just building isolated models
Strong understanding of AI-led data modeling, feature design, event instrumentation, and analytical data foundations required for scalable model deployment
Expertise in SQL, Python/R, BI tools (Tableau, Power BI), and modern data warehousing / transformation ecosystems
Ability to work effectively across Product, Engineering, Business, and leadership teams, translating complex analysis into action
MBA / Engineering / Statistics / Economics from a reputed institute

Nice to have

CFA or equivalent financial market understanding is a plus

Benefits

Opportunity to build and scale a high-impact analytics and decision intelligence function at the forefront of India's digital investing ecosystem
Chance to shape how Paytm Money uses data, AI, and customer intelligence to build the next generation of wealth products, user experiences, and growth engines

Additional details

Paytm is India's leading payments Super App, offering consumers and merchants a wide range of financial services
Paytm is a pioneer of the mobile QR payments revolution, with a mission 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
Paytm Money is owned by One97 Communications, founded by Vijay Shekhar Sharma
The company is headquartered in Noida and backed by leading global investors
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 KRAs / Success Metrics: Improvement in conversion rates across onboarding, KYC, activation, and trading / investing funnels
Key KRAs / Success Metrics: Growth in active traders, active investors, AUM, and repeat participation
Key KRAs / Success Metrics: Increase in trading frequency, engagement depth, and customer retention
Key KRAs / Success Metrics: Improvement in CAC, LTV, ARPU, and overall unit economics
Key KRAs / Success Metrics: Accuracy, adoption, and business impact of predictive models and AI-driven recommendations
Key KRAs / Success Metrics: Faster and higher-quality decision-making across Product, Growth, and Business teams
Key KRAs / Success Metrics: Increased adoption of self-serve, automated, and AI-enabled analytics across functions
Key KRAs / Success Metrics: Stronger data foundations and analytical readiness for advanced modeling and personalization use cases
Key Skills: Financial markets, broking, and investment analytics
Key Skills: Growth, funnel, and lifecycle analytics
Key Skills: Predictive modeling, AI/ML, and decision systems
Key Skills: AI adoption across analytics and business workflows
Key Skills: Data modeling for BI, experimentation, and AI use cases
Key Skills: Monetization, pricing, and unit economics analytics
Key Skills: Product analytics, personalization, and recommendation thinking
Key Skills: Business acumen and executive stakeholder influence
Key Skills: Data storytelling, visualization, and structured problem solving
Key Skills: Leadership, team building, and cross-functional execution
What Success Looks Like: Building a best-in-class analytics and AI-powered decision engine for a digital wealth platform
What Success Looks Like: Driving measurable impact on user growth, engagement, retention, monetization, and customer experience
What Success Looks Like: Enabling Paytm Money to make faster, smarter, and more scalable data-backed decisions
What Success Looks Like: Embedding predictive and AI-led thinking into core business, product, and growth workflows
What Success Looks Like: Creating a durable competitive edge through analytics, intelligence, and AI-enabled personalization in the broking and wealth ecosystem
Paytm has 500M+ users and a massive distribution advantage