Data & Analytics Lead

About this role

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Skills you'll use

What you'll do

• As a Product Analytics Lead, you will transform data into strategic insights by owning product analytics, risk and fraud monitoring, and analytics infrastructure. You will design and maintain scalable data pipelines, build self-service dashboards, apply AI-driven analytics to automate workflows, and ensure the reliability of data across commerce and payments. Working cross-functionally, you will help optimize product performance, improve customer experience, and enable informed business decisions.
• [Key Responsibilities]
• Own product analytics: conversion funnels, cohort retention, drop-off analysis, and user segmentation. Define event taxonomy, enforce instrumentation quality, and keep the tracking plan clean at scale.
• Own risk and fraud analytics. Build detection models and monitoring for suspicious transaction patterns, account abuse, promo/cashback exploitation, and chargeback anomalies. Work with ops to define rules and escalation triggers.
• Apply AI and automation to analytics workflows: anomaly detection on key metrics, automated alerting, predictive models for churn or fraud, and LLM-powered data querying to reduce manual reporting load.
• Build and manage data pipelines end to end. Design ingestion, transformation, and orchestration workflows to ensure clean, reliable, queryable data across commerce, payments, and wallet systems.
• Monitor product health and reconciliation gaps, and settlement tracking. Surface actionable insights for engineering and partner escalations.
• Build and maintain dashboards in Metabase for product, ops, and leadership. Make self-serve analytics real for non-technical teams.
• [Required Experience]
• 6+ years in product/growth analytics at a consumer tech or fintech company. You’ve owned the analytics function.

Who can apply

• 6+ years in product/growth analytics at a consumer tech or fintech company. You’ve owned the analytics function.
• Deep SQL proficiency across normalized schemas. Hands-on experience with Metabase or a comparable BI tool (Looker, Redash, Superset).
• Experience building and maintaining data pipelines: workflows, scheduling, data quality checks, and schema management.
• Strong understanding of event-driven analytics, tracking plans, and the discipline to keep them clean.
• Experience with risk, fraud, or abuse detection: rule-based systems, anomaly detection, or transaction pattern analysis.
• Familiarity with applying AI/ML to analytics problems: classification models, anomaly detection, or LLM-based automation for reporting and data ops.
• Experience with payment or transaction data: success/failure analysis, reconciliation, or financial reporting.
• [Preferred Qualifications]
• Experience with cloud data warehouse.
• Python for scripting, ad-hoc analysis, or lightweight ML models.

About Krafton India

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