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About Grab and Our Workplace
Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.
Job DescriptionYou'll lead the Data Science function supporting Lending, overseeing credit risk scoring models across PD, LGD, and collections. You'll guide the team in leveraging alternative data to improve model accuracy and signal. You'll lead the full model lifecycle—driving strategy, standards, and execution across model development, validation, deployment, and monitoring. You'll partner with business, risk, and ops leaders to shape the credit roadmap and influence decisions with data-driven insights. You are experienced in leading teams while being hands-on when needed. This role is suited for professionals with 10+ years of experience in data science and risk analytics.
You will report to Head of Data Science and this role is onsite based in Bangalore.
The Critical Tasks You Will Perform
- Lead the team in building predictive models to separate good vs bad borrowers using ML and traditional methods
- Drive development of ML and deep learning models for loss estimation across portfolios
- Oversee model validation and performance monitoring across diverse data sets
- Guide feature engineering strategies and explore new external data sources
- Champion model governance in collaboration with risk, compliance, and audit teams
- Ensure timely identification of performance drifts and lead model refresh cycles
- Communicate modeling outcomes and trade-offs to senior leadership and key stakeholders
- Translate analytics into strategic levers—policy, pricing, targeting, and credit expansion
- Set the vision for solving hard data science problems using best-in-class ML techniques
The Essential Skills You Need
- Deep domain expertise in credit risk modelling across PD, LGD, EAD, and collections
- Prior experience working in FinTech/credit businesses, especially in digital lending or unsecured loan portfolios
- Proven track record of applying data science to credit underwriting, risk segmentation, and portfolio management
- Expertise in Python for ML model development, with experience building scalable, production-grade solutions
- Proficient in Spark, SQL, and large-scale distributed data processing frameworks
- Grasp of advanced ML concepts, including model interpretability, bias mitigation, and performance optimization
- Experience with ML/DL libraries (scikit-learn, XGBoost, TensorFlow/PyTorch) and guiding teams on their best use
- Working knowledge of MLOps and orchestration tools (Airflow, MLflow, etc.), with experience standardising model deployment pipelines
- Exposure to LLMs and Generative AI, with a perspective on their potential applications in credit risk
- Design of robust, reusable feature pipelines from structured and unstructured data sources
- Familiarity with Git, CI/CD, and model versioning frameworks as part of scaling DS delivery
- Mindset and ability to coach team members through complex modelling issues
- With experience aligning technical outputs with business strategy
Life at Grab
We care about your well-being at Grab, here are some of the global benefits we offer:
- We have your back with Term Life Insurance and comprehensive Medical Insurance.
- With GrabFlex, create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
- We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
What we stand for at Grab
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
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