Lead Quantitative Analytics Specialist

Job Summary Responsibilities

• Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense of highly statistical theory
• Qualify monitor markets and forecast credit and operational risks
• Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives
• Utilize stochastic, structured securities, spread analysis, with the expertise in the theory and mathematics behind the analysis
• Review and assess models inclusive of technical, audit, and market perspectives
• Identify structure and scope of review
• Enable decision making for product and marketing with broad impact and act as key participant to develop and document analytical models
• Collaborate and consult with regulators and auditors
• Present results of analysis and strategies

Required Qualifications

• 5+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Desired Qualifications

• Overall experience around 5+ years in similar role
• Bachelors degree or higher in a quantitative field such as Computer Science, Applied mathematics, engineering, statistics, finance or econometrics from top tier institutes
• Strong problem-solving skills
• 5+ years of experience in credit risk analytics with exposure to statistical and machine learning model development, implementation or ML Ops
• 2+ years of advanced programming expertise in SAS
• 5+ years of advanced programming and debugging skills in Python OOP, packaging, build and deployment, data structures and algorithms, decorators, logging, exception handling, JIT compilers
• 2+ years of experience in High performance computing, Big Data and real time solutions PySpark, MapR streaming, parallel processing, real time optimization.
• 2+ years of experience in unit testing, UAT testing, regression testing and code review
• Comfortable with Git, GitHub, CI/CD pipelines and UNIX commands
• Excellent verbal, written, and interpersonal communication skills
• Strong ability to develop partnerships and collaborate with other business and functional areas
• Knowledge and understanding of issues or change management processes
• Experience determining root cause analysis
• Detail oriented, results driven, and has the ability to navigate in a quickly changing and high demand environment while balancing multiple priorities
• Understanding of bank regulatory data sets and other industry data sources
• Ability to research and report on a variety of issues using problem solving skills
• Exposure to banking domain in Credit Risk area on Retail/Commercial portfolio

Job Expectations

• Implement optimized ML solutions
• Perform various complex activities related to predictive modeling process enhancements and Python conversions
• Provide engineering and analytical solutions across model development, implementation, monitoring and production in a Big Data environment
• Support implementation of python based solutions for real time and/or batch based Machine Learning scorecard models for consumer and commercial banking
• Identify opportunities and deliver process improvements, standardization, rationalization and automations