Job Description:
This person will be a part of Retail Risk Analytics team targeted towards leveraging new age concepts like - Machine Learning- for the bank's real life business problems. This team will work across various risk analytics functions to develop enhanced capability in advanced analytics for retail risk teams and beyond.
Key Responsibilities of the role:
- Help deliver POCs from various risk sub functions by providing subject matter knowledge of advanced analytics & Machine learning techniques.
- Execute advanced analytics projects hands on in collaboration with various risk and RBWM sub functions
Leverage Data Labs to explore and recommend new tools for usage.
- Help support training & development of internal workforce to build a strong team of data scientist
Skills/Experience Required :
- Hands on experience of applying various machine learning techniques in real life problems
- Familiar with a majority of the techniques below and proven expertise in a few of them is a - MUST-
Neural Network/ Artificial Neural Network)
Random Forest
Gradient Boosting
Logit boosting
Apriori Algorithm
Support vector Machine
Recommendation Learning
Self-Organizing maps
Pattern recognition
- Interact effectively with a wide variety of people with varying backgrounds and responsibilities, including other credit risk analysts, project managers, operations personnel,
Conduct training and developmental activities.
- Bachelor's / Master's Degree in discipline like IT/Software, Engineering, Mathematics, Statistics, Economics etc from a reputed university
- 8-12 years in the analytics industry; of which at least 5 years of experience in machine learning/ high end analytical techniques
- Should be competent with techniques like neural network, boosting, support vector machine, random forest etc (hands on experience required)
- Good organizational, project management, analytical, problem-solving and verbal/written communication skills
- Comfortable working with the onshore/offshore team
- Good Knowledge of SAS/ R programming would be highly preferred
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