Looking for professionals having 2-9 years of experience in Banking domain and should have strong hands on Predictive modeling and machine learning techniques with good experience in SAS and Python for a Banking Giant. (Female Preferred)

Job Description:

- Looking for an energetic, motivated professional to join its Fraud Modelling team as fraud risk specialist. In this role, you will be tasked to prevent financial crime by developing fraud mitigation models.

Primary responsibilities include :

- Analysis of customer data and transactional data to identify emerging fraud trends, develop, and improve fraud models and perform model diagnostics.

- Collaborate with Product, Risk, Compliance and Technology to build and deploy a seamless Fraud risk decision framework

- Creating roadmaps towards the deployment of production level machine learning applications.

- Drive the innovation agenda through continually researching and evaluating emerging technologies and methodologies.

- Perform gap analysis to identify system weaknesses and mitigating measures.

- Drive the end to end testing approach - including test case documentation, review and monitoring of rule performance, fine-tune rules - and strategy implementation.

- Take ownership of existing models and undertake regular optimization and monitoring.

- Perform due diligence on model development governance framework and coordinate with Model risk management for approval and implementation

- Lead and manage complete model development life cycle and advise team members to develop best in class models.

Job Skills/Qualifications:

- Bachelor's degree in Statistics, Economics, Finance, Mathematics or a related quantitative field

- Engineering /MBA background from a premier institute.

- Minimum 6+ years of hands-on analytical experience.

- Minimum 4+ years- experience in statistical analysis with working knowledge of at least one of the following statistical software packages: SAS(Must), R or Python

- Model development and governance experience in any domain.

- Proficiency in designing, developing and maintaining supervised and unsupervised modeling techniques like Logistic / Linear Regression, Random Forest, XG Boost etc.

- Ability to handle large volumes of transactional data. Familiarity with data extraction tools like Hive will be an advantage. Should be pro-efficient in driver analysis and feature extraction.

- Successful candidate will have a demonstrable analytic, problem solving, and leadership skills, and has the ability to deliver projects in a fast-paced environment.

- Excellent presentation skills to eloquently explain and drive the agenda.

- Strong verbal and written communication skills; experience with stakeholder management

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