Lead Data Scientist- Collections Campaign

Role Overview: Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. As a Lead Data Scientist at Yubi, you will play a crucial role in designing, developing, and deploying ML/RL models to optimize collection campaigns across digital, telecalling, and field channels. Your expertise in machine learning and Python programming will be instrumental in driving innovation and delivering business value to clients. Key Responsibilities: - Develop, implement, and maintain advanced analytical models and algorithms to enhance debt collection processes. - Collaborate with business stakeholders to identify data-driven opportunities for improving debt collection strategies. - Continuously optimize existing models and incorporate new data sources to enhance the approach. - Take ownership of metric design, tracking, and reporting related to debt collection performance. - Enhance and update metrics to align with business objectives and regulatory requirements. - Effectively communicate findings to both technical and non-technical stakeholders. - Mentor and support junior data scientists to foster a culture of continuous improvement and collaboration. - Lead cross-functional teams on data projects to ensure alignment between data science initiatives and business goals. - Stay updated on industry trends, tools, and best practices in analytics and debt management. Qualifications Required: - Experience: 8 to 10 years - Technical Skills: - In-depth understanding of machine learning algorithms and their application to risk. - Expertise in statistical analysis, hypothesis testing, regression analysis, probability theory, and data modeling techniques. - Proficiency in Python, with working knowledge of PySpark. - Experience in building and deploying models on cloud platforms (AWS) and developing backend microservices using Fast API. - Familiarity with model explainability techniques and regulatory compliance for risk models. - Experience with NLP techniques is a plus. - Domain Skills: - Prior expertise in debt collection processes, credit risk assessment, and regulatory compliance is beneficial. - Ability to design, implement, and optimize performance metrics tailored for debt recovery initiatives. - Education and Experience: - Bachelors/Advanced degree in Data Science, Statistics, Mathematics, Computer Science, or related field. - 5 to 8 years of experience in data science and machine learning domain. - Experience in the financial sector or Collection team is advantageous.,