Lead Quantitative Analytics Specialist - #968903
The Wells Fargo Foundation
About Wells Fargo India
enables global talent capabilities for Wells Fargo Bank NA., by supporting business lines and staff functions across Technology, Operations, Risk, Audit, Process Excellence, Automation and Product, Analytics and Modeling. We are operating in Hyderabad, Bengaluru and Chennai locations.
Department Overview:
The Retail Credit Risk Modeling team is responsible for development and monitoring of Retail Credit Risk models across all portfolios
Responsibilities:
- Develop best-in-class transactional and application credit risk models leveraging cutting-edge advanced AI/ML techniques
- Analyze and explore predictive patterns using big data translating into enhanced strategies/rules
- Use Python to build end to end Machine Learning models starting from data processing to feature engineering to model estimation.
- Lead and participate in critical debugging, testing and performance tuning for machine learning and/or statistical models written in python code
- Conduct ad-hoc analysis and reporting as required
- Responsible for documenting and presenting detailed model development processes and results, suitable for a variety of audiences
- Lead and participate intensive team discussions, interactions with cross-functional teams, and dialogues with internal reviewers (Model Validation and Internal Audit)
- Collaborate with key business model users to ensure models are business driven, properly implemented and executed
- Respond to ongoing analytical requests from auditors and regulatory reviewers in timely manner
Essential Skills:
- 8+ years of scorecard development, credit or fraud risk analytics experience, or a combination of both.
- Master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science
- 8+ years of programming experience in SAS, Python or R
- Experience in credit and/or fraud risk model development and/or monitoring
- Excellent verbal and written communication skills
- Experience in producing high quality technical documentation with tools such as Excel, Word, PowerPoint
Desired Skills:
- Ph.D. in economics, statistics, finance, math, engineering or similar quantitative disciplines
- 8+ years of experience in Credit Risk Model development
- Exposure to machine learning techniques (Random Forest, XG Boost, Light GBM, Neural Networks, and so on)
- Developed Credit Risk Model using Python
- Familiarity with Credit Bureau Data
- Excellent problem solving skills and ability to connect dots, see big picture and find solutions and articulate in a clear manner.
- Understanding of process, methodologies used in credit/fraud scoring model development, implementation, validation and monitoring
- Ability to effectively manage multiple assignments with challenging timelines
- Experience in statistical modeling techniques
Posting End Date:
8 May 2024We Value Diversity
At Wells Fargo, we believe in diversity, equity and inclusion in the workplace accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in US:All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
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To request a medical accommodation during the application or interview process, visit.
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