About This Role
Wells Fargo is seeking a Quantitative Analytics Specialist. For
Model monitoring Quant -MPAM.
This role will conduct routine market and counterparty risk models performance monitoring.

Market and Counterparty Risk Analytics (MCRA)
is responsible for developing models for MCRM's Corporate Market Risk Group, Enterprise Counterparty Risk Management, and Market and Counterparty Capital. MCRA also includes a model governance and quality assurance function, as well as a model management function that manages ongoing modeling activities for the supported business groups.

In This Role, You Will
Develop, implement, and calibrate various analytical models
Perform highly complex activities related to financial products, business analysis and modeling
Perform basic statistical and mathematical models using Python, R, SAS, C++ and SQL
Perform analytical support and provide insights regarding a wide array of business initiatives
Provide solutions to business needs and analyze workflow processes to make recommendations for process improvement in risk management




Collaborate and consult with peers, colleagues, managers, and regulators to resolve issues and achieve goals

Required Qualifications:
2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Master's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:
2+ years of quantitative analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, education
Master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics or computer science
Education/experience in quantitative areas
Robust analytical skills with high attention to detail and accuracy
Knowledge in finan

📌 Quantitative Analytics Specialist Hyderabad
🏢 Wells Fargo
📍 Hyderabad