Job Description

: Qualifications: Master's degree in economics, mathematics, engineering, operations research or related areas is preferred; candidates with BA/BS degrees from the top tier academic institutions are also welcome to apply 4 - 10 years of experience in credit risk modelling/ analytics preferably in Banking and Financial Services. ( Experience in US banking domain is a plus) Hands-on experience working on Machine Learning models to solve analytical use cases. Experience with credit risk models like PD, customer management models, EAD, LGD, loan management, underwriting models, collection models . Excellent problem-solving and analytical skills, with the ability to work on complex projects and deliver high-quality results. Proficiency in some of the key programming languages such as Python, PySpark, Advance SQL, Hive and experience with ML related libraries. Experience with large-scale data/ Big data processing and distributed computing frameworks is a plus. Strong communication skills, both written and verbal, with the ability to convey complex ideas to diverse stakeholders Responsibilities: Design, implement, and optimize sophisticated machine learning models in Banking domain for Credit Risk modeling. Your work will enable us to uncover meaningful patterns, trends, and insights from data, ultimately solving analytical use cases. Utilize sophisticated algorithms and methodologies to refine and improve the performance of credit risk/ marketing mix models, enabling accurate predictions and actionable outputs for analytical use cases. Stay updated on the latest machine learning advancements, actively identifying and integrating cutting-edge techniques to continuously improve our models and address diverse analytical use cases. Provide data-driven insights and recommendations that support decision-making processes and enable us to overcome analytical hurdles. Document your methodologies, experiments, and results in clear and concise terms. Effectively communicate complex concepts and findings to both technical and non-technical stakeholders. Stay informed about the latest advancements in machine learning (ML) and assist in identifying and adopting cutting-edge techniques. Enable us to continuously enhance our ML models and effectively address diverse analytical use cases.