Quantitative Analytics Specialist (#001887)

Wells Fargo Bank•Charlotte, NC

12d•Hybrid

About The Position

Wells Fargo Bank N.A. seeks a Quantitative Analytics Specialist in Charlotte, NC. This role involves the development of state-of-the-art methods in quantitative modeling using statistical and machine learning (ML) techniques and implementing them in efficient/scalable algorithms. The specialist will identify cutting-edge techniques in academia and industry for applications in risk management, drive the development of methodology and algorithms through applied research, and disseminate best practices across the quantitative modeling community within the bank. Responsibilities include developing a model library to support the validation process, designing and implementing benchmark models, and automating their use on an advanced computing platform. Collaboration with internal and external quantitative communities is expected to stay current with the latest developments and practices in quantitative risk. The role also involves facilitating credible challenge of processes/models, developing the skills of the validation team, building collaborative working environments, and leading process improvement projects. The specialist will develop, implement, and calibrate various analytical models, perform highly complex activities related to financial products, business analysis, and modeling, and build basic statistical and mathematical models using Python, R, C++ and SQL. Additionally, the role provides analytical support and insights for business initiatives, offers solutions to business needs, and analyzes workflow processes to recommend improvements in risk management. Telecommuting is permitted up to 2 days a week, but the position requires in-person presence at the work address. Travel is not required.

Requirements

Master's degree in Finance, Econometrics, or a related discipline.
2 years of experience in the job offered or in a related quantitative analytics role. Experience can be gained concurrently with graduate level coursework.
Skills can be gained through work experience or graduate level coursework.
Experience in modeling (statistical and machine learning), optimization, algorithm development, programming, and coding including Generalized Linear Models, Gradient Boosting Machines, or Random Forrests, the related training and optimization algorithms, and their common implementations in R and/or Python.
In-depth knowledge of Machine Learning/Deep Learning algorithms and their implementation such as XGBoost or LightGBM as well as Neural Networks, including Multilayer Perceptrons or Convolutional Neural Networks.
Strong computing and programming background and knowledge of one or more languages including Python, Java, or R.
Experience with Machine Leaning/Artificial Intelligence computing platforms and tools TensorFlow and Keras.
Experience with Graphics Processing Unit (GPU) programming, multi-core, or distributed programming.
Ability to work with large datasets and experience in database management and tools such as Hadoop, Spark or SQL.

Responsibilities

Be involved in the development of state-of-the-art methods in quantitative modeling using statistical and machine learning (ML) techniques and implementing them in efficient/scalable algorithms.
Identify cutting-edge techniques in academia and industry as well as implementing them for applications in risk management.
Drive the development of methodology and algorithms by conducting applied research.
Disseminate best practice across the quantitative modeling community within the bank.
Develop model library to support validation process.
Design benchmark models and implement and automate their use within our advanced computing platform.
Collaborate with internal and external quantitative communities to keep abreast of latest developments and practices in quantitative risk.
Facilitate credible challenge of the processes/models and to develop the skills of the validation team.
Be capable of building collaborative working environments including leading process improvement projects.
Develop, implement, and calibrate various analytical models.
Perform highly complex activities related to financial products, business analysis and modeling.
Build basic statistical and mathematical models using Python, R, 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.