Senior Quantitative Analytics Specialist (Senior GenAI Data Scientist)

Wells Fargo & Company•Charlotte, NC

5d•$139,000 - $260,000•Onsite

About The Position

Wells Fargo is seeking two Senior Quantitative Analytics Specialists (Senior GenAI Data Scientists) to support driving natural language processing (NLP) and large language model (LLM) initiatives as part of the Consumer Model Development group. This role is ideal for a seasoned professional passionate about building scalable Natural Language Processing (NLP) solutions and driving innovation in large language model (LLM) applications.

Requirements

4+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

Nice To Haves

Advanced analytical and strategic thinking skills with the ability to assess complex data, systems, architectures, and data flows to identify risks and opportunities and drive timely, data driven decision making
Demonstrated ability to work independently, take initiative, and exercise sound judgment within a matrixed environment to drive business outcomes
Advanced collaboration, influencing, and communication skills with the ability to build trusted relationships, align stakeholders, and communicate complex information effectively across all organizational levels, including executive leadership
Demonstrated expertise in artificial intelligence (AI), including Generative AI, large language models (LLMs), Natural Language Processing (NLP), prompt engineering, multimodal models, and retrieval augmented generation, with experience leading initiatives from concept through production
Experience with Agile practices, product management principles, and modernization initiatives, including automation and cloud transformation, within financial services or other regulated environments
Familiarity with regulatory and compliance considerations related to artificial intelligence (AI) model development, validation, and deployment within regulated environments
Knowledge of model evaluation metrics, performance optimization techniques, and responsible artificial intelligence (AI) practices, including bias mitigation
Proficiency in Python with experience using machine learning (ML) frameworks (such as PySpark, PyTorch, TensorFlow), Natural Language Processing (NLP) libraries (such as Hugging Face, NLTK, spaCy), and cloud platforms including Google Cloud Platform (GCP), Vertex AI, or Azure
Strong data science capabilities across data engineering, model development, deployment, and production support, including experience leveraging artificial intelligence (AI) and automation tools such as Generative AI and GitHub Copilot to improve efficiency and delivery outcomes

Responsibilities

Apply advanced statistical theory to quantify, analyze, and manage data, financial risks, and market dynamics through robust modeling approaches
Collaborate and partner with internal and external stakeholders to gather requirements and deliver impactful, data driven and AI enabled solutions
Engage with Audit, Model, Product, and technical stakeholders to ensure transparency, governance, and compliance with regulatory expectations
Establish and promote best practices across the model lifecycle, including development, evaluation, validation, deployment, and monitoring, while contributing to analytical strategies, modeling approaches, and forecasting methodologies through execution of complex model development and implementation
Drive innovation through proof-of-concept initiatives, experimentation, and applied research, while staying current on emerging trends in Generative AI, large language models (LLMs), and Natural Language Processing (NLP), and translating insights into practical business applications
Guide and mentor junior team members, fostering technical excellence, collaboration, and knowledge sharing
Lead the design, architecture, and implementation of scalable artificial intelligence (AI), machine learning (ML), Natural Language Processing (NLP), and large language model (LLM) solutions using advanced techniques, modern frameworks, and cloud native tools to support complex language understanding use cases