Job DescriptionJob ID: MJ000271
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.
