Lead Quantitative Analyst, Structured Finance Analytics

As a Lead Quant Analyst at Morningstar DBRS Structured Finance Analytics team, your role involves managing a team of offshore Quant Analysts to automate data analysis processes, develop data analytics tools for rating, research, and surveillance, and optimize workflow tools. You should have a strong understanding of Structured Finance products (RMBS, ABS, CMBS etc.) and possess technical skills in Python, Tableau, AWS, Athena, SQL, and VBA. Your primary responsibility will be to lead and mentor the team, manage work priorities, maintain communication with global teams, and ensure high-quality deliverables. Key Responsibilities: - Manage and mentor a team of 6-8 Quant Analysts, guiding them on technological enhancements and fostering a collaborative work environment. - Prioritize work schedules, monitor performance, and report project progress regularly. - Lead the team in transforming, improving, and integrating data as per business requirements. - Understand data storage concepts and implement quick solutions to align development activities with business needs. - Participate in design and build phases to ensure high-quality deliverables. - Drive process efficiencies and ideate automations for better workflow. - Collect, organize, and analyze data for criteria development, ratings, and research reports. - Ensure compliance with regulatory and company policies and procedures. Qualifications Required: - Minimum of 9-11 years of relevant experience in Credit Modeling / Model Validation roles. - MBA (Finance)/BTech/PHD (Math) from a Tier I college. - Knowledge of finance, statistics, and behavioral sciences. - Strong analytical skills with experience in large databases/datasets. - Expertise in Python, Tableau, Microsoft Excel, MSSQL, and Data Science stack (Jupyter, Pandas, NumPy). - Experience with SAS/R is a plus. - Familiarity with AWS infrastructure is an advantage. - Ability to manage multiple tasks simultaneously and deliver results promptly. Morningstar DBRS is an equal opportunity employer. As a Lead Quant Analyst at Morningstar DBRS Structured Finance Analytics team, your role involves managing a team of offshore Quant Analysts to automate data analysis processes, develop data analytics tools for rating, research, and surveillance, and optimize workflow tools. You should have a strong understanding of Structured Finance products (RMBS, ABS, CMBS etc.) and possess technical skills in Python, Tableau, AWS, Athena, SQL, and VBA. Your primary responsibility will be to lead and mentor the team, manage work priorities, maintain communication with global teams, and ensure high-quality deliverables. Key Responsibilities: - Manage and mentor a team of 6-8 Quant Analysts, guiding them on technological enhancements and fostering a collaborative work environment. - Prioritize work schedules, monitor performance, and report project progress regularly. - Lead the team in transforming, improving, and integrating data as per business requirements. - Understand data storage concepts and implement quick solutions to align development activities with business needs. - Participate in design and build phases to ensure high-quality deliverables. - Drive process efficiencies and ideate automations for better workflow. - Collect, organize, and analyze data for criteria development, ratings, and research reports. - Ensure compliance with regulatory and company policies and procedures. Qualifications Required: - Minimum of 9-11 years of relevant experience in Credit Modeling / Model Validation roles. - MBA (Finance)/BTech/PHD (Math) from a Tier I college. - Knowledge of finance, statistics, and behavioral sciences. - Strong analytical skills with experience in large databases/datasets. - Expertise in Python, Tableau, Microsoft Excel, MSSQL, and Data Science stack (Jupyter, Pandas, NumPy). - Experience with SAS/R is a plus. - Familiarity with AWS infrastructure is an advantage. - Ability to manage multiple tasks simultaneously and deliver results promptly. Morningstar DBRS is an equal opportunity employer.