Why Join Us
Be part of one of Southeast Asia's most ambitious superapps a homegrown tech company solving real-world challenges across transportation, food delivery, logistics, fintech, maps, and more. We operate with millions of transactions per day, and we're now investing heavily in cutting-edge data science to drive our next phase of growth.
You'll build models and decisioning systems that shape customer onboarding, credit access, and portfolio healthserving millions of transactions and users across multiple domains (fintech and beyond).
What You'll Do
You'll lead the technical data science workstream for online lending products, owning end-to-end delivery from problem framing to production impact:
- Credit risk & scoring: Build, validate, and improve credit risk models, and define how scores drive decisioning.
- Propensity & onboarding intelligence: Develop propensity models across the funnel (conversion, activation, repeat borrowing, churn) and optimize onboarding flows.
- Full lifecycle ownership: Translate business goals and risk constraints into clear technical problems; drive experimentation, deployment, performance tracking, and iterative improvement.
- Data & production readiness: Own the technical side of data processing for lending use-casesfeature definitions, data quality checks, pipeline requirements, reproducible training, model monitoring, and documentation needed for safe operation.
- Collaborative impact: Partner with Product, Risk, Engineering, Operations, and Leadership to embed models into core lending workflows and ensure adoption.
- Broader DS opportunities: As capacity allows, you may also pursue DS applications in other domains (e.g., fraud scoring, behavioral prediction, ranking/recommendation, pricing/optimization) aligned with company priorities.
What You Bring
- Strong technical foundation: Bachelor's or higher in Computer Science, Statistics, Applied Math, or a related field. Master's or PhD preferred.
- Industry experience: Experience with credit risk modeling and data science in financial products.
- Coding skills: Proficient in Python or similar languages for data science; solid grasp of SQL and cloud platforms (e.g., GCP, AWS).
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- Modeling depth: Hands-on with supervised learning for scoring (e.g., XGBoost/LightGBM), calibration, handling imbalance, leakage control, and robust offline/online evaluation.
- Problem solving: You can turn messy business questions into implementable technical solutions, and know how to ship reliable systems.
- Communication: Comfortable explaining trade-offs, model behavior, and results to nontechnical stakeholders (Product/Risk/Leadership) and driving alignment.
