Senior Manager, Data Science

2 weeks ago


Bangalore Karnataka, India Grab Full time

Company DescriptionAbout Grab and Our WorkplaceGrab 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 You 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 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 QualificationsThe Essential Skills You Need 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 Ability to lead 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 Strong problem-solving mindset and ability to coach team members through complex modeling issues With experience aligning technical outputs with business strategy Additional InformationLife at GrabWe 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 GrabWe 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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