Innovation Digital Data Products & Insights Lead Data Scientist for SDI AI/ML/NLP Data Science team

Key Skills

Agile methodologies, Big Data Technology, Confluence, Data pipelines, Deep Learning, Github, Machine learning techniques, Project management, PySpark, Python Programming, PyTorch, Statistical modeling

Job Description

Wells Fargo is looking for a highly motivated and collaborative person who can work with all levels of the organization, senior business and technology leaders, and product, operations, and finance managers.

About this role:

At Wells Fargo, we are looking for talented people who will put our customers at the center of everything we do. We are seeking candidates who embrace diversity, equity and inclusion in a workplace where everyone feels valued and inspired. Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you.

The Strategy, Digital & Innovation (SDI) team forges the strategic direction of Wells Fargo, provides and manages our digital foundation and common capabilities, and transforms our business models to meet evolving customer needs and grow the company. The investments we are making will help the company create innovative digital banking experiences and make it easier for customers to achieve their financial goals.

This role is part of the AI/ML/NLP data science team within the Strategy, Digital & Innovation (SDI) LOB. This role will help enable the organization to operate in a manner consistent with its vision, goals, and objectives. The Strategy, Digital and Innovation (SDI) team forges the strategic direction of Wells Fargo, provides and manages our digital foundation and common capabilities, and transforms our business models to meet evolving customer needs and grow the company. The investments we are making will help the company create innovative digital banking experiences and make it easier for customers to achieve their financial goals.

In this role, you will:

• Responsible for model development, documenting the development and monitoring processes and model performance
• Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense, of highly statistical theory
• Partner with internal business partners and senior leaders to frame the problem and define/identify business objectives
• Data visualization including presenting results of analysis and strategies.
• Communicating the results
• Adhere to corporate model risk policy and ensure compliance with model risk management
• Working with other data science teams to identify, gather, retain, and publicize modeling artifacts required for approved and repeatable processes (reproducible results)
• Contribute to data science teams group effort to stay concurrent with the cutting edge and bleeding edge of fraud/NLP/ML/DL algorithms, methodologies in the open-source community
• Work with AI technology and production teams to operationalize models
• Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives
• 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
• Collaboratively design, develop, and deploy AI/ML models using state of the art NLP and ML techniques available in the open stack and/or vendor solutions
• Develop an understanding of CMoR’s Model Risk Management Program, Policies, and Processes

Required Qualifications:

• 5+ 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 a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science

Desired Qualifications:

• Hands on familiarity with machine learning and statistical modeling techniques applying machine learning techniques such as neural networks, random forest, GBM and SVM, Probabilistic Graphical Models, Deep Learning architectures, using open-source languages like Python, PySpark, PyTorch
• Hands on experience writing data processing and data pipeline for model development including gathering and building datasets to collect intents, joining and aggregating source datasets, cleaning messy data, designing feedback loop on data need
• Detail oriented. Experience with model governance requirements. Able to de-mystify AI models to make them transparent and explainable
• Big Data experience
• Meeting facilitation experience in leading discussions that result in consensus and commitment
• Ability to work and influence successfully within a matrix environment and build effective business partnerships with all levels of team members
• Experience in producing high quality technical documentation with tools such as Excel, Word, PowerPoint
• Attention to detail in both analytics and documentation
• GitHub experience
• Agile/Confluence experience
• Project management experience.