Lead Quantitative Analytics Consultant - NLP / GenAI

About Wells Fargo
Wells Fargo & Company (NYSE: WFC) is a diversified, community-based financial services company with $2.0 trillion in assets. Founded in 1852 and headquartered in San Francisco, Wells Fargo provides banking, insurance, investments, mortgage, and consumer and commercial finance through more than 8,500 locations, 13,000 ATMs, the internet (wellsfargo.com) and mobile banking, and has offices in 42 countries and territories to support customers who conduct business in the global economy. With approximately 273,000 team members, Wells Fargo serves one in three households in the United States. Wells Fargo & Company was ranked No. 25 on Fortune's 2017 rankings of America's largest corporations. Wells Fargo's vision is to satisfy our customers' financial needs and help them succeed financially. News, insights and perspectives from Wells Fargo are also available at Wells Fargo Stories.
About Wells Fargo India
Wells Fargo India enables global talent capabilities for Wells Fargo Bank NA., by supporting business lines and staff functions across Technology, Operations, Risk, Audit, Process Excellence, Automation and Product, Analytics and Modeling. We are operating in Hyderabad, Bengaluru and Chennai locations.
Department Overview
Product, Analytics and Modeling (PAM) brings a team member-based approach to the international talent pool focusing on analytical requirements. PAM encompasses roles that deliver meaningful insights, analysis and reporting based on skills, experience and judgment to support the Enterprise Analytics and Data Science (EADS) organization operate successfully today, and continue on the right trajectory to operate successfully in the future.
About the Role
Analytic Consultant/Data Scientist is a partner-facing role and is responsible for delivering high impact analytic and data science projects by using analytics, in support of operational risk initiatives across Chief Operating Office org.
AIM (AI Innovation and Modeling) is the central analytics group tasked with solving high-impact business challenges and standing up cutting-edge analytical capabilities to be shared across Wells Fargo's analytic community.
We are looking for a high performer to join our team and help us solve challenging and interesting business problems through rigorous data analysis and predictive modeling. In this highly consultative and visible role, you will support development analytic projects from multiple business lines using various technology and techniques ranging from but not limited to supervised, unsupervised and semi-supervised machine learning, deep-learning, NLP, optimization algorithms in both edge nodes and in big data environments (like hortonworks, MapR, Aster etc.)
Responsibilities:

• Person would be required to work individually or as part of a team on data science projects and work closely with business partners across the organization.
• Person will be leading small group of junior data scientists and work as a technical lead to support enterprise grade Gen AI programs
• He/she would be developing statistical/machine learning/GenAI models using various techniques (supervised, unsupervised, semi-supervised) and technologies including but not limited to SAS, R, Python, Spark, H2O, Aster etc.
• Work closely with data engineers, BI and UI specialists and deliver top notch analytical solution for the bank.
• Define business problem and translate it into analytical problem.

Essential Qualifications:

• 8+ years of experience in the field of data science and analytics with hands on working knowledge in related tools and technologies.
• Deep working knowledge of machine learning and deep learning, including model selection, training dynamics, overfitting, and evaluation tradeoffs.
• Practical mastery of prompt engineering and LLM behavior control, including structured prompting, chain-of-thought, robustness, and security considerations.
• Ability to design and optimize Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, chunking, and reranking techniques.
• Experience with model evaluation, monitoring, and Responsible AI, including bias, hallucination mitigation, governance, and human-in-the-loop validation.
• Strong MLOps/LLMOps capabilities, covering model lifecycle management, experiment tracking, deployment, cost control, and latency optimization.
• Proficiency in Python-based data science and software engineering, with experience integrating GenAI into scalable, production-grade systems.

• Exploratory Data analysis
  • Provide exploratory data analysis using Python/R/SAS / SQL
  • Experience with Databases like oracle , Teradata, Sql server
  • Advance Excel skills
  • Data integration and clean up data for the usage
  • Experience with structured data and semi-structured text or Excel files

• Business Intelligence
  • Tableau, Power BI, Shiny, Dash, HTML5
• Business Analytics
  • Data mining and Insights
  • Trend Analysis , forecasting and pattern recognition
  • Find opportunities in the data and able to communicate to the partners
  • Consult with partners to define issues/information needs
  • Present findings to multiple levels of management
  • Ensure that analyses are delivered on time, while surpassing partner expectations
  • Ensure partner transparency throughout the life of the project
  • Proactively seek opportunities to increase the value of analysis
  • Strong oral and written communication and consultative skills

Desired Qualifications:

• Strong collaboration skills
• Output deployment using appropriate technologies (HTML5, Shiny, Django)
• Working expertise in Tensorflow, Keras or Pytorch would be added advantage
• Ability to translate analytical data into useful business information
• Critical thinking and strong problem solving skills
• Ability to learn the business aspects quickly
• knowledge of banking industry and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
• Knowledge of functional area such as risk, marketing, operations or supply chain in banking industry.
• Ability to multi-task and prioritize between projects
• Ability to work independently and as part of a team