The Mission
Direct Lending is a fast-growing, award-winning lending fintech on a mission to empower people's lives with simple and responsible financing. Over the past four years we have disbursed 60,000 needs-based financing cases across car repair/ service, motor insurance renewal and medical — and we are now scaling our loan book and working toward IPO readiness by 2030.
We are looking for a Senior Data Scientist — Credit Risk Modelling to own, improve, and evolve our credit decisioning models. You will work closely with our data science, product, and tech teams to ensure our straight-through approval engine is accurate, scalable, and continuously improving — enabling us to grow approval rates without increasing default, while expanding access to financing for bank-underserved segments through the use of alternative data.
Why Join Direct Lending
- Real impact on a live portfolio. 60,000 financing cases and growing. Your model improvements translate directly into business outcomes — more customers approved, healthier portfolio, simpler lives. You will see the impact of your work in real numbers, not in a sandbox.
- Lean, move fast, own your work. You will have direct access to relevant historical dataset from day one, the autonomy to drive your own agenda, and the ability to test, iterate, and deploy without bureaucracy slowing you down. Decisions are made in days, not months.
- Pioneer alternative data for financial inclusion. You will have the opportunity to build something genuinely meaningful — using non-traditional data signals to extend financing to Malaysians who are underserved by conventional credit assessment. This is applied data science with real social impact
- Breadth beyond credit. While credit risk modelling is the primary focus, you will have the opportunity to explore data science applications across other business areas — including merchant analytics, collections optimisation, and customer behaviour modelling.
- IPO journey. A profitable company on a clear path to 2030. This role is positioned at Senior level with a clear path to leading the data science function as Direct Lending scales.
What You Will Own
1. Credit Model Development & Improvement
- Own, maintain, and continuously improve our existing straight-through approval model
- Identify opportunities to improve approval rate without increasing default — through better feature engineering, model architecture, and variable selection
- Build and validate new credit scorecards and risk models as the business scales into new products.
- Conduct champion-challenger testing to validate model improvements before deployment
2. Alternative Data & Financial Inclusion
- Identify, source, and evaluate alternative data sources to improve credit assessment for bank-underserved segments
- Engineer features from unstructured and semi-structured alternative data sources to enhance model predictive power
- Build responsible and explainable models that extend financing access without compromising portfolio quality
- Stay current on alternative data trends and emerging credit signals relevant to the Malaysian market
3. Model Monitoring & Risk Analytics
- Build and maintain a model monitoring framework to track performance, stability, and drift — flagging deterioration in approval rate, default rate, and NPL trends proactively
- Conduct deep-dive analysis on portfolio segments to identify risk concentration and emerging credit trends
- Provide data-driven insights to support credit policy decisions
4. Collaboration & Stakeholder Engagement
- Work closely with the existing data science team, providing guidance and technical direction to the senior data scientist where relevant.
- Collaborate with tech and product teams to ensure new data signals are captured and integrated into the modelling pipeline
- Work closely with the credit team to align model outputs with credit policy, validate findings against real-world portfolio behaviour, and ensure decisioning models reflect the risk appetite of the business.
- Present model performance, findings, and recommendations clearly to senior leadership
Who You Are
- Experience: 5–8 years in data science with a strong focus on credit risk modelling. Hands-on experience in unsecured consumer lending, BNPL, credit card is essential. Experience working with alternative data sources is a strong advantage.
- Technical skills: Proven experience building, validating, and deploying credit scorecards and machine learning models for credit decisioning. Strong proficiency in Python and SQL is required. Experience with model monitoring frameworks is preferred.
- Credit domain knowledge: You understand how consumer credit models work end-to-end — from data preparation and feature engineering to scorecard development, validation, and deployment. You think in terms of portfolio outcomes.
- Alternative data mindset: You are curious about non-traditional data signals and have experience or strong interest in applying them to credit assessment. You understand the responsible use of alternative data and how to validate its predictive value.
- Ownership & Collaboration: You don't wait to be told what to improve — you monitor, question, and bring recommendations forward. You work well across data science, product, credit, and tech teams, translating complex model outputs into clear business recommendations.