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| Job Location | Bangalore |
| Education | Not Mentioned |
| Salary | Not Disclosed |
| Industry | Banking / Financial Services |
| Functional Area | Investment Banking / M&A |
| EmploymentType | Full-time |
- Own end-to-end risk model development efforts within Core Modeling using advanced statistical/mathematical techniques like regression, XG Boost, Nueral nets, SVM or other traditional modeling/ machne learning methods.- Lliasoning with risk development partners like MRGR, Fair Lending, Legal, Technology and LOB Business Stakeholders- Own quality of models developed, assuring accurate and appropriate model development standards, and right implementation of models. - Efficiently design and produce muliple models parallely following procedures for model development, validation, and reporting- Provide support for model implementation, performance monitoring and calibration.- Expected to work on multiple projects with limited guidance - Accountable for business impact and robustness of solutions delivered. Handle a variety of analytic projects as well to support modeling efforts and business needs. Such projects may include data research and leveraging models to solve business problems - Accountable for business impact and robustness of modelss delivered. - Reviews work of subordinates and is responsible for their work output. - Supports hiring, training and development of team resources Qualifications:6+ years statistical model development/ Machine learning model developmentexperience in a deeply quantitative role in the financial services industry or Fintechs dealing with advanced analytical or machine learning methods6+ years SAS experience; well versed in SAS/Base, SAS/STAT, SAS/Macro, and data-mining procedures. SAS Certification preferred.- OR - 3+ years experience inPython, Spark, Hive, Scala, Big Data, Hadoop, Keras, Scikitlearn etc.Demonstrates leadership within the team in terms of attitude, initiative and inclusivity. - Ability to make contributions to the group s knowledge base by proposing new and creative ways for approaching quantitative modeling problems and model design.- Experience utilizing relational and distributed data tools dealing with structured and unstructured data. Example tools include Hive, DB2, Oracle, or Teradata.- A Master s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university like IIT, IIM, IISc, ISI, IGIDR etc.- Experience in developing, implementing and testing traditional and Machine Learning driven risk models within financial institutions. - Ability to deliver high-quality results under tight deadlines and be comfortable with the manipulation, analysis, and summarization of large quantities of data. - Well-developed oral and written communication skills. ,
Keyskills :
big datarisk modelsdata researchknowledge basecomputer sciencemachine learningmodel developmentcommercial modelsfinancial servicesbehavioral training