Senior Manager Data Science

As a Data Science Manager / Senior Data Science Manager , you will lead the Platform Data Analytics DS squad, accountable for payment performance, fraud and abuse prevention, bot defense, and Cross-sell optimization, user journey, search & recommendation, UGC integrity and etc. In this role, you will bridge data analytics, rigorous experimentation, and cutting-edge ML / LLM solutions to scale our core platform capabilities and power data-driven growth across Traveloka. What You’ll Do Team Leadership: Lead, coach, and scale a team of Data Scientists and ML Engineers, fostering a culture of technical excellence and business impact. Analytics & Experimentation: Drive platform-wide analytics and design rigorous A/B experimentation frameworks to evaluate product features, user journey optimizations, and system changes. ML & AI Solutions: Oversee the design, development, and deployment of machine learning, deep learning, and GenAI/LLM-based applications across platform domains. Strategy & Execution: Partner closely with Product, Engineering, and Operations leaders to translate broad business goals into scalable data science roadmaps. Requirements Experience: 7+ years in data science, ML, or quantitative analytics, including 3+ years directly leading high-performing Data Science / ML teams in tech or e-commerce environments. Analytics & A/B Testing: Solid background in Data Analytics and A/B experimentation, with a track record of driving business outcomes through data-driven insights. Applied ML: Production experience with gradient-boosted trees and anomaly/outlier detection on high-volume, highly imbalanced data. Comfortable owning models where the cost of a false positive is a business number, not an abstraction. LLM / GenAI application development: Hands-on experience building and — critically — evaluating RAG or agentic systems. Must be able to describe how they measured whether an LLM system was actually working Preferred Qualifications: Domain Experience: Prior domain experience in OTA or Marketplace; familiar with Fraud & Abuse Prevention, user analytics, or Payment Systems, etc. Technical Methods: Graph-based methods (GNNs, entity-linkage) for fraud rings or coordinated abuse. Education (Advanced): Advanced degree (Master's or Ph.D.) in Data Science, Machine Learning, Statistics or a related field is preferred.