Company DescriptionAbout Grab and our workplaceGrab is Southeast Asia\'s leading superapp. We are dedicated to improving the lives of millions of users across the region by providing them everyday services such as deliveries, mobility, financial services, enterprise services and others. More than that, we provide the opportunity for them to have a better life. And that aspiration starts inside Grab because we believe in a seamless blend of work and home life, making every aspect of life better for all.Guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles-the 4Hs: Heart, Hunger, Honour and Humility-we work to create economic empowerment for the people of Southeast Asia. With our unwavering commitment to our values, we believe that we\'re more than a service provider; we\'re agents of positive change.Get to know our Team:GrabFin is an aggregate of FinTech businesses spread across 6 countries in S.E. Asia, in the Payments, Lending and Insurance domains. Our engineering teams are stationed in Bangalore, Singapore as well in countries like Indonesia and Vietnam. We are incredibly excited about the opportunity 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 deep understanding of our ecosystem combined with world class engineering execution continues to create tremendous value for our customers.GrabFin\'s data science team is stationed across Bangalore and Singapore. We aim to hire a Data Scientist to join our Bangalore office to augment the existing Bangalore team. The data scientist will work in a relatively flat team structure with an independent mandate to build and manage critical data science models on a day to day basis. The candidate can expect to solve hard technical problems and grow into an expert on both batch and real time Data Science use cases. The right candidate will have passion for technology and data science along with being a natural problem solver. This role is based out of Grab\'s Bangalore office.The Role:Develop a deep understanding of Payments architecture and its nuances across use cases /countries to build predictive models for payments processor selection, multi-objective routing, intelligent transaction retry, downtime management, payment method recommendation and other such solutions to optimize payments processing and reduce failure rates.Develop cross use case expertise to enable machine learning solutions across other business verticals within Grab Financial like Lending and Insurance.Manage and own the entire end-to-end lifecycle of building and validating predictive models along with their deployment and maintenance.Interface with commercial, compliance & operation teams to formulate solutions & product changes informed by your findings and business inputs/reality.Work independently or in a team to solve complex problem statements.Individual contributor role with 5-8 years of experience expected.The day-to-day activities:Build predictive models using a mix of machine learning and traditional analytics methods.Validate models on new datasets, based on in-market performance.Engineer predictive features from internal data assets to build refined transaction and customer profiles. Identify external data assets to bring into the model mix.Solve previously unsolved problems using best in class data analytics and machine learning methodologies.Work backwards to conceptualize and design model frameworks to solve business problems.Build and maintain dashboards for model performance KPIs.QualificationsThe Must Haves:Expert in building machine learning and predictive models in Python and Spark is an absolute must.Demonstrated experience in real time implementation of machine learning solutions using Flink SQL, Flink SDK and PythonSQL, Presto, Hive proficiency.Sound knowledge of machine learning concepts. Illustrative machine learning concepts/methods are: Bagging, Boosting, Regularisation, Online Learning, Recommendation Engines, Reinforcement Learning, etc.Experience on LLMs, and Generative AI is a plusExpert in feature creation on a variety of data types.Understands the trade-offs between model performance and business needs.Strong problem-solving mindset is critical for success in this role.Self-motivated, independent learner, team playerAdditional InformationBenefits at Grab:We care deeply about your well-being and are committed to supporting you every step of the way. Here are some of the global benefits we offer:
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