Xendit provides payment infrastructure across Southeast Asia, with a focus on Indonesia and the Philippines. We process payments, power marketplaces, disburse payroll and loans, provide KYC solutions, prevent fraud, and help businesses grow exponentially. We serve our customers by providing a suite of world-class APIs, eCommerce platform integrations, and easy to use applications for individual entrepreneurs, SMEs, and enterprises alike.

Our main focus is building the most advanced payment rails for Southeast Asia, with a clear goal in mind — to make payments across in SEA simple, secure and easy for everyone. We serve thousands of businesses ranging from SMEs to multinational enterprises, and process millions of transactions monthly. We’ve been growing rapidly since our inception in 2015, onboarding hundreds of new customers every month, and backed by global top-10 VCs. We’re proud to be featured on among the fastest growing companies by Y-Combinator.

Build fraud prevention products to secure payments across Southeast Asia.


The fraud team is building innovative fraud prevention products with the aim of securing online payments for businesses across Southeast Asia. By leveraging AI and machine learning on the vast amounts of transactional and identity data collected across the years, we are well positioned to help reduce the ~USD 300 million(1.6% of revenue) in losses due to fraud experienced by Southeast Asia’s businesses. This will help businesses focus on growth without fearing about losses due to fraud.

Data analysts in the fraud team are responsible for deriving insights from the multiple data sources that we have to inform product decisions with regards to feature engineering, model building etc. They are also responsible for building monitoring dashboards to detect potential fraud and therefore helping our merchants and Xendit to prevent losses due to fraud.

We are looking to expand the suite of features for our fraud detection product and are looking for passionate data analysts who have a deep interest in data, applied sciences or machine learning  to join our team in building a scalable fraud prevention product for Southeast Asia. Join us if you would like to contribute to our cause in making the payment space safer and free of fraud and abuse.

  1. Build and maintain analytic dashboards
  2. Identify common traits of fraud and build dashboards and alerting systems around these traits to detect and prevent fraud
  3. Build dashboards to monitor product metrics such as chargeback rates, chargeback type breakdown etc.
  4. Monitor merchants transactional behaviour and identify potential fraudulent activity that may result in losses for Xendit
  5. Design, analyze, and interpret the results of experiments. Create hypotheses on potential fraud prevention methods and conduct exploratory data analysis to prove hypothesis - outcomes of analysis will guide the heuristics and features using in our risk scoring engine
  6. Work with data scientists to build and improve our machine learning models
  7. Create and maintain a system that enables clean labelling/flagging of fraudulent data which can be used to power our machine learning models.
  8. Be the driver to explore new data sources that can improve our fraud detection system
  9. Do whatever it takes to make Xendit succeed
You may be a good fit if
  • Bachelor’s Degree in a quantitative or technical field
  • 2+ years of hands-on experience as a data analyst/scientist
  • Experience with SQL, data analysis, visualization tools (e.g. Looker), or programming languages such as Python/R
  • Experience solving analytical problems using quantitative approaches
  • Experience delivering insights from data analysis to stakeholders
  • Experience doing data analysis in a big data environment (Spark, Presto, Hive)
  • Basic knowledge of using Python/R for data analysis and visualization
  • Great ownership mentality - taking initiative to improve our fraud prevention methods (finding new data sources, creating new data features etc.)
  • You thrive on autonomy and have proven you can push towards a goal by yourself
  • Pick up new skills quickly using a combination of reading and self-exploration
  • You communicate well across teams
  • Prior experience with payments, risk or fraud prevention/detection systems
  • Experience in building machine learning models

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