Lead Quantitative Analytics Specialist (#001756)

Wells Fargo•New York, NY

5d•$191,000 - $305,000•Hybrid

About The Position

Wells Fargo Technology sets IT strategy; enhances the design, development, and operations of our systems; optimizes the Wells Fargo infrastructure footprint; provides information security; and enables continuous banking access through in-store, online, ATM, and other channels to Wells Fargo’s more than 70 million global customers. Wells Fargo Bank N.A. seeks a Lead Quantitative Analytics Specialist in New York, NY. This role involves leading complex initiatives including the creation, implementation, documentation, validation, articulation, and defense of highly statistical theory. The specialist will qualify and monitor markets, forecast credit and operational risks, strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives. Expertise in stochastic, structured securities, and spread analysis, along with the underlying theory and mathematics, is required. The role includes reviewing and assessing models from technical, audit, and market perspectives, identifying the structure and scope of reviews, and enabling decision-making for products and marketing with broad impact. The specialist will be a key participant in developing and documenting analytical models, collaborating and consulting with regulators and auditors, and presenting analysis results and strategies. Additionally, the role involves designing, developing, and deploying AI/ML models using state-of-the-art techniques in the open stack (Python/PySpark/PyTorch) and/or vendor solutions. This includes partnering with LOB leads to frame problems, explore ML/DL model architectures and methodologies, generate artifacts for the model development life cycle (MDLC), author model development documents, and deliver AI models that meet business needs. Adherence to corporate model risk policy and ensuring compliance with model risk management are crucial. The role also involves working with other data science teams to identify, gather, retain, and publicize modeling artifacts for approved and repeatable processes, and collaborating with AI technology and production teams to operationalize models. Effective work within agile project management methodologies for data science is expected, along with knowledge sharing on topics like machine learning algorithms, hyper-parameter tuning/search, and traversing multiple big data platforms. Contribution to the CoE data science team's effort to stay current with cutting-edge NLP/ML/DL algorithms and methodologies in the open-source community and vendor solutions is also a key aspect.

Requirements

Master's degree in Civil Engineering, Mathematics, Statistics, or related quantitative field.
Five (5) years of experience in the job offered or in a relation position involving quantitative analytics experience.
Five (5) years of statistical modeling experience
Experience with Machine Learning
Experience with Natural Language Processing (NLP)
Experience with Python
Experience with Spark
Experience with SQL
Hadoop/Big Data experience

Responsibilities

Lead complex initiatives including creation, implementation, documentation, validation, articulation and defense, of highly statistical theory.
Qualify monitor markets and forecast credit and operational risks.
Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives.
Utilize stochastic, structured securities, spread analysis, with the expertise in the theory and mathematics behind the analysis.
Review and assess models inclusive of technical, audit, and market perspectives.
Identify structure and scope of review.
Enable decision making for product and marketing with broad impact and act as key participant to develop and document analytical models.
Collaborate and consult with regulators and auditors.
Present results of analysis and strategies.
Design, develop, and deploy AI/ML models using state of the art techniques available in the open stack (Python/PySpark/PyTorch) and/or vendor solutions.
Partner with LOB leads to frame the problem, explore various ML/DL model architectures and methodologies, generate required artifacts related to model development life cycle (MDLC), author the model development document, and deliver AI models that meet business needs.
Adhere to corporate model risk policy and ensure compliance with model risk management.
Work with other data science teams to identify, gather, retain, and publicize modeling artifacts required for approved and repeatable processes.
Work with AI technology and production teams to operationalize models.
Work effectively in an agile project management methodologies for data science.
Knowledge sharing with members of the team and across the organization on topics including machine learning algorithms, hyper-parameter tuning/search, and traversing across multiple big data platforms.
Contribute to the CoE data science team's group effort to stay current with the cutting edge NLP/ML/DL algorithms, methodologies in the open source community and vendor solutions.