Credit Data Scientist & Analytics - Experiments
Xendit provides payment infrastructure across Southeast Asia, with a focus on Indonesia, the Philippines and Malaysia. 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 and within 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.
About the Job
Our Experiments team builds growth engines for Xendit through experimental products by pushing the frontiers of what is possible in the industry and creating modern solutions to old problems. As part of the Experiments team, you may become part of a team that builds from 0 to 1, from 1 to 1,000, or from 1,000 to the moon. This role requires flexibility and adaptability to take on new challenges to grow an early-stage product. The team presents a unique opportunity for people who are looking to build something new, rather than scaling mature products. People who join this team have a strong interest in growth (personally and professionally) and, therefore, a continuous improvement mindset.
We're looking for a passionate individual who is willing to do whatever it takes to help us build credit models for innovative products that our local market has not seen before.
Minimum Qualifications
- Bachelor’s Degree in a quantitative field (e.g., Data Science, Mathematics, Statistics, Econometrics, Computer Science, Engineering, or Artificial Intelligence).
- 3+ years of work experience developing application, behavioral and collection scorecards for unsecured consumer credit products.
- Solid oral and written communication skills, especially around analytical concepts and methods.
- Demonstrated experience collaborating with product managers and backend engineers for the execution and monitoring of tests or decisioning processes.
- Lead and mentor: Guide data scientist team members in analyzing complex datasets to extract meaningful customer insights.
- Collaborate: Work closely with business analysts to translate data-driven insights into actionable business strategies.
- Problem-solving: Strong passion to convert business challenges into modeling opportunities, ensuring alignment with strategic goals.
- Model Development: Oversee the full lifecycle of production model development, from conception to implementation, in partnership with MLOps, Data Engineering, and Engineering teams.
- Model Maintenance: Ensure the continuous monitoring and optimization of credit models to maintain effectiveness.
- Strategic Testing: Design and execute tests to evaluate customer behavior patterns and validate business hypotheses.
- Capability to work effectively under pressure, meeting tight deadlines with proactive, decisive, and adaptable approaches.
Preferred Qualifications
- 3+ years of work experience with Machine Learning or Statistical Modeling.
- 3+ years of work experience in data science and Python and SQL programming for data analysis.
- Working experience with AWS.
- Experience managing projects independently.
- Self-motivated with intellectual curiosity.
- Proven ability to quickly learn new technologies, concepts, and tools.
Responsibilities
1. Collectively build a credit business from the ground up with new, innovative products not yet available in the market
2. Modeling Independently:
- Design and develop models as standalone products.
- Collaborate with risk and business stakeholders to understand objectives.
- Collect, process, and transform data.
- Investigate new data sources.
- Build, demonstrate, measure, and optimize performance end-to-end modeling solutions.
3. Analytical Problem Solving:
- Convert vague contexts and phenomena into structured analytical problems.
- Utilize statistical knowledge, machine learning models, and visualization techniques to extract meaningful insights from data related to business goals and strategies.
- Design and conduct experiments and A/B testing for measuring business initiatives.
- Perform regular testing analysis.
4. Innovation:
- Embrace and adapt to rapid technological and scientific advancements.
- Explore advanced tools and information sources to aid the team in understanding and solving problems and maintaining products.
