Quantitative Analytics Manager
Wells Fargo (company site, opens in a new tab)10+ yrsHyderabadOn-siteDirect career pageEst. 30–55 LPA
Posted 7h ago
Reach people at Wells Fargo
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Wells Fargo you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead and manage a team responsible for model monitoring of consumer decision models, ensuring high-quality delivery and end-to-end monitoring across all consumer lending portfolios.
- Determine work scope and prioritization, manage allocation of people and financial resources, and resolve operational risk issues impacting business, product, and marketing decisions.
- Develop strategy, policies, procedures, and organizational controls; interact with stakeholders including auditors and regulators; mentor and guide direct reports, assisting in hiring talent.
Minimum Requirements
- 10+ years of quantitative analytical experience demonstrated through work experience, training, education, or equivalent.
- 3+ years of leadership experience with formal or informal reportees.
- Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, or computer science.
- Expertise in SAS and Python modelling pipelines; experience in credit and/or fraud risk model development and monitoring for consumer products like Credit Cards, Personal Loans, Home Loans, Auto Loans.
Ideal Candidate Profile
- Experienced in managing quantitative analytics teams focused on credit risk scoring and model monitoring in consumer lending.
- Strong technical proficiency in SAS and Python with hands-on experience in credit/fraud risk modeling and production of high-quality technical documentation.
- Able to navigate complex regulatory environments and audit interactions, with a strategic approach to risk identification, escalation, and mitigation in a fast-paced setting.