CRISIL - Quantitative Analyst - Machine Learning

Role

- Use AI/machine learning and advance analytical techniques to solve banking and financial problems such as default prediction, financial crime (fraud detection, anti-money laundering etc.), consumer analytics, credit ratings, mortgages, credit score, sentiment analysis etc.,

- Model development, validation and implementation of machine learning and statistical models such as Random Forests, Support Vector Machines, Neural Networks and Boosting algorithms.

- Efficient automation of large scale data analysis, model development and validation in various platforms or applications.

- Stay current with latest algorithms, AI techniques and their applications in finance and banking with specific focus in financial crime compliance;

- Liaison with senior management and key stakeholders on potential projects in the areas of AI/Machine Learning across banking and financial sectors.

- Exposure in machine learning libraries like tensor flow, spark, pytoch

Candidate Profile

- Graduate (Ph.D. / Master- s) degree in a quantitative discipline, such as Operations Research, Statistics, Applied Mathematics, Computer Science, or similar field

- 2-4 years of relevant experience in Machine Learning including implementation of several algorithms

- Practical applications of AI/Machine Learning in different domains including Financial Crime Compliance is preferred but not mandatory

Essential Qualifications

- Excellent knowledge of analytical/statistical/machine learning models with in-depth understanding of complex aspects of model(s) implementation

- Should have worked extensively on Analytical tools - R/Python, SAS and should have strong experience in SQL programming

- Excellent understanding of the FCC/AML industry and their applications across business lines

- Excellent verbal and written English communication skills

- Good at creating and delivering impactful presentations

Preferred Qualifications

- Exposure to any one of the industry standard vendor tools for AML - Mantas, Actimize-SAM, SAS-AML or Norkom

- Additional exposure to Tableau, Teradata and TIBCO Spot fire preferred

- Analytical/numerical expertise demonstrated by cutting edge quantitative/statistical research and analysis projects by understanding the mathematical concepts behind the implementation.