Data Science Manager at Grab

Short Description:

The Data Science Manager role at Grab in Bangalore involves leading a team in credit risk scoring, driving predictive model development, and collaborating across teams for data-driven solutions. Requirements include 8+ years' leadership experience, proficiency in predictive modeling, machine learning, and a commitment to inclusivity in the workplace.

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Job Title: Data Science Manager

Location: Bangalore (Salarpuria Aura)

Employment Type: Full Time

Job Requisition ID: R-2023-9-0058

Job Overview:

At Grab, we believe in The Grab Way, a set of principles that guide us in achieving our mission of creating economic empowerment for the people of Southeast Asia. We are looking for a Data Science Manager to join our team at the Salarpuria Aura office in Bangalore.

About Us:

Grab Financials' Lending business is a key part of Grab's ecosystem, providing micro-credit and loan products to our drivers and merchants. Over the past four years, this business has become a significant profit driver for Grab. The Lending team consists of experts in Fintech Lending across multiple Southeast Asian countries. With a presence in Singapore, Malaysia, Vietnam, the Philippines, Indonesia, and Thailand, the Lending operations are structured regionally and by country to effectively manage responsibilities.

The Role:

As a Manager, Data Science at Grab Financial, you will lead a team of experts in Lending, Data Science, Banking, and Credit Risk Scoring. You will collaborate closely with teams in Singapore, working on risk, data science, engineering, analytics, and operations. Your primary responsibilities will include building machine learning models for credit risk scoring and LGD (Loss Given Default), among others.

Key Responsibilities:

• Develop a deep understanding of driver, agent, and merchant behavior to create predictive models for credit risk, collections, and SME analytics.
• Manage the entire lifecycle of model development, from creation to validation, deployment, and maintenance.
• Collaborate with business, risk, and operation teams to propose solutions and product changes based on data-driven findings.
• Lead a team with a minimum of 8 years of experience.
• Work independently or collaboratively to address complex problem statements.
• Create predictive models using machine learning and traditional analytics methods.
• Validate models on new datasets based on real-world performance.
• Engineer predictive features from internal data assets and consider external data sources.
• Tackle previously unsolved analytics problems using state-of-the-art data analytics and machine learning techniques.
• Communicate effectively to share insights and lead the execution of the analytics roadmap.
• Lead a small team reporting to you.

Requirements:

• A deep understanding of driver, agent, and merchant behavior for predictive modeling.
• Experience managing the end-to-end lifecycle of predictive model development.
• Strong interface with business, risk, and operation teams for proposing data-driven solutions, especially in collections and SME risk analytics.
• A minimum of 8 years of relevant team leadership experience.
• Proficiency in machine learning and traditional analytics methods.
• Ability to validate models on new datasets based on real-world performance.
• Expertise in feature engineering and external data integration.
• Knowledge of machine learning and statistical modeling concepts.
• Experience in building machine learning analytics models.
• Understanding of the trade-offs between model performance and business needs.
• Strong problem-solving skills and the ability to lead a team.
• Self-motivated and an independent learner who enjoys sharing knowledge with team members.
• Knowledge of technology in fintech lending is a plus.

Our Commitment:

At Grab, we are committed to creating a diverse and inclusive workplace that values all Grabbers, regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, and other unique attributes. We believe in enabling every Grabber to perform at their best.

Please click here to apply.