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Quantitative Analytics Specialist 2 -Statistical Modeling and Machine Learning

Job Description
At Wells Fargo, we want to satisfy our customers’ financial needs and help them succeed financially. We’re looking for talented people who will put our customers at the center of everything we do. Join our diverse and inclusive team where you’ll feel valued and inspired to contribute your unique skills and experience.
Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you.

Corporate Risk helps all Wells Fargo businesses identify and manage risk. The team focuses on several key risk types, including conduct, credit, financial crimes, information security, interest rate, liquidity, market, model, operational, regulatory compliance, reputation, strategic, and technology risk.
The group provides leadership, enhances communications, assists with problem identification and solutions, and shares best practices. In addition, the group provides an enterprise-wide view of risk, assists management and our Board of Directors in identifying and monitoring risks that may affect multiple lines of business, and takes appropriate action when business activities exceed the risk tolerance of the company.
Wells Fargo’s Corporate Model Risk (CMoR) organization is seeking a highly qualified person to join its Advanced Technologies for Modeling (AToM) Group as a Quantitative Analytics Specialist 2 (QAS2). The person will be part of the Statistical Modeling and Machine Learning team within AToM. The responsibilities of the AToM group include development of cutting-edge models, algorithms, visualization tools, and a computing platform to advance the Bank’s practice in the areas of credit, operational, and market risk management. 
This individual will be involved in the development of state-of-the-art methods and algorithms in machine learning (ML), artificial intelligence (AI), and advanced statistics with the goal of driving best modeling practice across the bank. Specific duties include, but are not limited to, the following:
  • Identify state-of-the-art techniques in the literature on ML/AI and advanced statistics and adapting them for applications in risk management
  • Drive new methodology development by conducting applied research in advanced statistical methods, ML/AI for emerging applications in risk modeling
  • Disseminating best practice across the quantitative modeling community within the bank through algorithm development, white papers, and seminars
  • Develop model library and platform to support model validation process
  • Design, implement and automate model replication, benchmarking and testing with CMoR’s advanced computing platform to improve effectiveness and efficiency
  • Collaborate with internal and external quantitative communities including academic community to keep abreast with the latest developments and practices in quantitative risk


Required Qualifications

  • A PhD in statistics, mathematics, physics, engineering, computer science, economics, or quantitative field; or a Master s degree in the above areas with 2+ years of experience in one or a combination of the previously mentioned fields above




Other Desired Qualifications
  • The ideal candidate will have a PhD degree in Statistics, Computer Science, Operations Research, Engineering, or a related quantitative field. We will also consider applicants who are about to graduate with a PhD by the end of December 2019. These candidates MUST complete their thesis defense and all other requirements for graduation by December 31, 2019.
  • Other qualifications include:
  • Experience and ability to demonstrate first-hand knowledge in several of these areas: modeling, statistical inference, computing, big data analytics, and machine learning
  • Excellent background in advanced statistical concepts, modeling, and data analysis techniques
  • In-depth knowledge of ML and AI methodologies such as ensemble algorithms, neural networks, supervised and unsupervised learning, and anomaly detection
  • Strong computing and programming background and knowledge of one or more languages such as Python, R and C++
  • Experience with ML/AI computing platforms and tools such as TensorFlow and Keras
  • Preferred experience with GPU programming, multi-core, or distributed programming
  • Ability to work with large datasets and some experience with database management and tools such as Hadoop, Spark and SQL
  • Good verbal and written communication skills as well as interpersonal skills
  • Ability to prioritize work, meet deadlines, achieve goals, and work under pressure in a dynamic and complex environment
  • Ability to develop partnerships and collaborate with other business and functional areas




Street Address
NC-Charlotte: 401 S Tryon St - Charlotte, NC
CA-SF-Financial District: 550 California St - San Francisco, CA


Disclaimer

All offers for employment with Wells Fargo are contingent upon the candidate having successfully completed a criminal background check. Wells Fargo will consider qualified candidates with criminal histories in a manner consistent with the requirements of applicable local, state and Federal law, including Section 19 of the Federal Deposit Insurance Act.



Relevant military experience is considered for veterans and transitioning service men and women.

Wells Fargo is an Affirmative Action and Equal Opportunity Employer, Minority/Female/Disabled/Veteran/Gender Identity/Sexual Orientation.