As the leading peer-to-peer lending platform in Southeast Asia, we fund SMEs and grow economies. We are building technology to fund Small and Medium Enterprises (SMEs) by identifying creditworthy firms for our Peer-to-Peer digital lending platform.
We are looking for you to join us this summer to build the next generation lending platform for SMEs in Singapore, Indonesia and Malaysia. We believe in great engineering practices, working together and learning from one another. We speak Python, Java, Node, .NET, Angular2, Scala, Postgres and command line.
We have a number of challenging and exciting problems in Product, Tech, Data and Mobile teams. In Product, we sit at the intersection of design, business and engineering, helping craft features and products to serve our users well. In Tech we are building exciting backend services and front-end tools which give our business teams the flexibility to launch new products at lightning speed. In Data we are building technologies to analyse financial data of SMEs - (bank statements, financials, etc) and individuals for the purpose of identifying creditworthy firms. In Mobile, we are building powerful, elegant Mobile apps to reach even the most remote borrowers and investors.
Your job role may be required to assist in the following tasks:
- Design and build informative data visualizations that help investors and borrowers track their progress
- Build attractive landing pages and measure their success
- Contribute to code and architecture reviews to keep our code quality high
- Debug and resolve production issues; take part in blameless postmortems
- Plan and execute smooth migrations to new technology as a way of managing technical debt
- Data collection, processing, visualisation, analysis
- Develop and implement actionable and accurate insights and present to the various business teams
- Test, measure, act on projects that drive the business
- Code up live dashboards for real time visibility of business
- Find innovative and creative ways to obtain new data
- Develop models for predictive studies