The Data team at Traveloka consists of Analytics, Data Platform, Data Science, Machine Learning / AI groups, individually comprising an expansively diverse team of data engineers, data analysts, data scientists, and product managers.
Data science plays a critical function in Traveloka's PayLater data team. Our internal credit approval and credit scoring models are a central part of the PayLater business and enable us to expand credit access to the underbanked. Our fraud detection models reduce the business's losses, ensuring sustainability.
The successful candidate will build the next generation of credit and fraud models for Traveloka PayLater that will impact the business by growing our current customer base and allowing us to extend to new markets.
The PayLater data team is part of the wider Financial Services (FinServ) group at Traveloka, which also covers Payments and Insurance. Within this group, the data team 1) builds and maintains machine learning models that optimise pricing, customer targeting and risk; and 2) provides analytics that drive business decisions. The PayLater data team often collaborates with other members of the FinServ data team.
- Given a business problem and requirements from business stakeholders, identify objective functions and ML algorithms that should be used.
- Understand the trade-offs of different modelling choices and have a point of view on the trade-offs. Communicate the choices to stakeholders and be able to support your point of view.
- Implement and deploy ML models in collaboration with the engineering team.
- Monitor and perform analytics on deployed models in order to evaluate model drift and assess when the model should be retrained.
- Communicate with stakeholders within the data team and the business team and bridge those requirements with the engineering team
- Share knowledge by mentoring, pairing, and collaborating with other other data scientist, analysts and engineers
- With at least 5 years of relevant experience
- Strong technical background, preferably from statistics, computer science, mathematics, or other quantitative backgrounds
- Experience in deploying machine learning models to production
- Strong programming ability, preferably in Python
- Proficiency with git, CI/CD, and deployment automation
- Good knowledge of SQL
- Experience with Google Cloud Platform is a bonus