PAY RANGE

Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to achievements, skills, experience, or work location. The range listed is just one component of the compensation package offered to candidates.
$144,400.00 - $300,000.00

APPLICANTS WITH DISABILITIES

To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo .

WELLS FARGO RECRUITMENT AND HIRING REQUIREMENTS:

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process

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 discipline such as mathematics, statistics, engineering, physics, economics, or computer scienc

Desired Qualifications:

• Strong statistical modeling or computer science background and hands on model development or validation skills
• Considerable knowledge of machine learning algorithms and their applications, including Random Forest, GBM, XGBoost, deep learning, NLP, computer vision, LLMs.
• Experience with building complex deep learning architectures such as MLPs, RNNs, CNNs and Generative AI frameworks such as RAG and Agentic AI.
• Strong Kaggle experience
• Extensive experience with Deep Learning and LLM frameworks such as PySpark,
• Experience in Load Balancing, GPU based processing, Performance Optimization
• Prior Experience leading teams and interacting with executive level stakeholders.
• Proven ability to Identify opportunities to integrate traditional ML techniques when appropriate and ensure a strong data science foundation is present in AI applications.
• Knowledge of financial industry general model development lifecycle is preferred but not required
• Prior experience working with Model Risk Management
• Demonstrated independence, teamwork and leadership skills
• Strong project management skills
• Excellent written and verbal communication skill

We’re seeking a highly technical leader who’s adept at advanced AI/ML algorithms and their applications in financial institutions to join our AI/ML Center of Excellence in Internal Audit. The individual must have a strong data science / computer engineering background and must be skilled in designing and deploying Machine Learning models using Python based frameworks. The Individual will lead a team that plays a critical role in providing internal audit with Artificial Intelligence Models across various business areas, such as Fraud, Credit Risk and Bank Operations.
As a Lead Quantitative Analytics Specialist (LQAS) you will play a crucial role in the development and maintenance of our data science and business intelligence solutions. This role will specialize in leading machine learning, deep learning, and generative AI initiatives that will be utilized by internal audit leaders to enhance and expedite decision-making and drive automation. You will provide expertise within and across business teams, demonstrate the ability to act as a technical mentor and lead project teams through the end to end model development lifecycle.

In this role, you will:

• Lead the definition of data science solution requirements, and drive innovation throughout the model development lifecycle using Deep Learning and LLM frameworks such as PySpark, SparkML, PyTorch, Tensorflow/Keras, MXNet, LangChain , Llamaindex
• Independently carry out tasks, using critical thinking and problem-solving skills to devise effective solutions.
• Design, train, and deploy supervised and unsupervised machine learning and deep learning models in a Unix based GPU environment using python (pytorch, transformers, BertTopic, unsloth, langgraph, langchain, etc..) to drive scalable solution development.
• Develop and advise on Large Language Model (LLM) solutions including RAG, and SFT.
• Extensive knowledge of github operations for project versioning, code development, and project management.
• Write clean, well-commented code for easy collaboration and maintain structured project documentation using GitHub and/or Jira.
• Technical model documentation experience (Model development documentation)

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 discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Desired Qualifications:

• Strong statistical modeling or computer science background and hands on model development or validation skills
• Considerable knowledge of machine learning algorithms and their applications, including Random Forest, GBM, XGBoost, deep learning, NLP, computer vision, LLMs.
• Experience with building complex deep learning architectures such as MLPs, RNNs, CNNs and Generative AI frameworks such as RAG and Agentic AI.
• Strong Kaggle experience
• Extensive experience with Deep Learning and LLM frameworks such as PySpark,
• Experience in Load Balancing, GPU based processing, Performance Optimization
• Prior Experience leading teams and interacting with executive level stakeholders.
• Proven ability to Identify opportunities to integrate traditional ML techniques when appropriate and ensure a strong data science foundation is present in AI applications.
• Knowledge of financial industry general model development lifecycle is preferred but not required
• Prior experience working with Model Risk Management
• Demonstrated independence, teamwork and leadership skills
• Strong project management skills
• Excellent written and verbal communication skills

Job Expectations:

• Hybrid work schedule
• This position is not eligible for Visa sponsorship