Job Description

Life at Grab At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia. Get to know the Team The Decision Systems team is responsible for implementing and maintaining the automated decision engine toolkits used by our lending business in Southeast Asia. We make an impact by managing our growing portfolio of customers’ credit lifecycle using Real-Time information and decisioning. Our team is made up of a group of highly skilled specialists who are trained on the latest decision engine software in the industry. If you are looking for a super charged and exciting career in the automated decisioning space of our lending business, then you should join our team! Get to know the Role:

• This is a regional role for Grab’s lending business based out of Singapore.
• Candidate should be hands on with decisioning tools (such as Power Curve, Strategy One), analytics, data management, simple statistical and econometric modelling and SQL ETL tasks.
• Working in close coordination with Data and Analytics team in Singapore, Regional Credit Risk team as well as Regional Collections team to build and deploy credit/collection decision journeys into production for the lending business in Grab.
• Strong grasp of Java and Java based software.
• Key day to day tasks:
  • Work on/with Decision Engine software e.g. Experian’s Power Curve (Credit Decision) and Strategy One (Collections) to build and deploy decision strategies for the lending business.
  • Support the Decision Engine Manager in the implementation of new projects in key markets as well as change requests.
  • Perform maintenance of the policy rules in the software and the optimization of business strategies.
  • Collaborate with engineering and product to help in software integration in a joint team effort
  • Work with Regional Collections team to be the bridge in terms of deploying ever evolving credit decision strategies into production
  • Work with data scientists, technology teams and other functions to deep dive on data solutions and prioritize business and information needs
  • Test for incoming data quality (exploratory data analysis) and build and execute UAT test cases at the point of deployment of analytics models
  • UAT of decision strategies before production.

The must haves:

• A Bachelor's/Master’s degree, preferably in Analytics, Statistics, Software, Mathematics, Economics, Computer Science or Engineering
• 3-4 years relevant work experience in Credit Decisioning software, Credit/Collections Solutions, project management
• Expert in SQL/Presto, strong JAVA skills
• Proficient with working on Big databases
• Proficient in Excel and PPT
• Strong analytical skills with the ability to collect, organize and analyse significant amount of information with attention to detail and accuracy
• Knowledge of data models, data mining and segmentation techniques
• Self-motivated and independent learner
• Detail-oriented and self-driven individual who thrives in a dynamic working environment
• Good in business communication

Our Commitment We are committed to building diverse teams and creating an inclusive workplace that enables all Grabbers to perform at their best, regardless of nationality, ethnicity, religion, age, gender identity or sexual orientation and other attributes that make each Grabber unique. About Grab Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. Today, the Grab app has been downloaded onto millions of mobile devices, giving users access to over 9 million drivers, merchants, and agents. Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries. Join us today to drive Southeast Asia forward, together.