Data & Analytics Lead

[ WHO WE ARE] Based out of South Korea, KRAFTON, Inc. is committed to discovering and globally publishing games that deliver a uniquely fun experience with global production studios known for distinctive creatives. Founded in 2007, KRAFTON consists of PUBG STUDIOS, Bluehole Studio, RisingWings, Striking Distance Studios, Dreamotion, Unknown Worlds, 5minlab, Neon Giant, KRAFTON Montréal Studio and ReLU Games, each trying to innovate the enjoyment of games through continuously embracing challenges and new technologies, expanding our platforms and services to win the hearts of more fans. KRAFTON India has established itself as a trailblazer in the gaming industry, driven by its unwavering commitment to delivering distinctive and enjoyable gaming experiences. At the heart of KRAFTON India's success lies a deep understanding of the Indian gaming community. The company takes immense pride in its premier entertainment properties that include highly popular titles like BATTLEGROUNDS MOBILE INDIA (BGMI), Road To Valor: Empires, Defense Derby, New State Mobile. These games have not only captured the hearts of Indian players but have also played a pivotal role in fostering a thriving e-sports ecosystem in the country.  With a focus on India, KRAFTON is dedicated to nurturing the gaming and start-up ecosystem. In line with this commitment, KRAFTON initiated the KRAFTON India Gaming Incubator (KIGI) in October 2023. KIGI aims to support 6-10 teams annually with program durations ranging from six months to one year. Additionally, KRAFTON India has invested over $150 million in the Indian market in the past two years and has committed an additional $150 million to the Indian start-up ecosystem over the next three years. [About the role]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. 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. Familiarity with UPI transaction data or India-stack payment systems. Experience with dbt, Airflow, or similar orchestration tools.