Lead Data Scientist

8 hours ago

Bengaluru, Karnataka, India Data Science Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Get to know the team

GrabFin is an aggregate of FinTech businesses spread across 6 countries in S.E. Asia, in the Lending, Payments and Insurance domains. We are excited to provide innovative financial services to all participants of the Grab Ecosystem be it our Drivers, Consumers or Merchants. Our products are built on fundamental market insights combined with data science and engineering to bring the best product market fit across the cross section of our user base. This understanding of our ecosystem combined with world class engineering execution continues to create tremendous value for our customers.

The data scientist will work in a relatively flat team structure with an independent goal of building and manage critical data science models daily. You can expect to solve hard technical problems and grow into an expert on both batch and real-time Data Science use cases. You will have experience with technology and data science.

You will be reporting to Senior Manager, Data Science.

This role is onsite based in Bangalore.

Get to know the role

You'll develop credit risk scoring models for consumer loans, including PD, LGD, and collection models. You'll work with alternative data sources to boost model signal and accuracy. Your role will involve full ownership of the end-to-end model lifecycle—from building and validation to deployment and maintenance. You'll collaborate with business, risk, and operations teams to shape solutions and influence product strategy with your insights. This is an individual contributor role suited for professionals with 8 years of experience.

The Critical Tasks You Will Perform

  • Build predictive models using a mix of machine learning and traditional analytics methods to segregate between Good vs Bad borrowers
  • Build Machine learning & Deep learning models to estimate losses from of a given portfolio.
  • Validate models on new datasets, based on in-market performance.
  • Engineer predictive features from internal data assets to build refined customer profiles. Identify external data assets to bring into the model mix.
  • Drive model governance by collaborating with risk policy, compliance, and audit teams to ensure adherence to regulatory expectations.
  • Identify model gaps or performance drifts and lead model refresh cycles.
  • Present findings to senior leadership with clear articulation of risk trade-offs and growth.
  • Translate model insights into strategic recommendations (e.g., policy changes, pricing levers, customer targeting strategies).
  • Solve previously unsolved analytics problems using best in class data analytics and machine learning methodologies.

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