Lead Quantitative Analytics Specialist
Wells Fargo is hiring a Lead Quantitative Analytics Specialist in Bengaluru, India. Level rates it ; you can apply on Wells Fargo.
Level's read on this job
- Little AI ●○○○ is how Level rates this role: aI is not part of the work.
- Tools and skills the posting cites include prompt engineering, RAG and MLOps.
- Level classifies it as a lead role.
- Level lists 1,500+ other open roles at Wells Fargo.
- The role was posted in the last few days.
AI in this role
About this role:
Wells Fargo is seeking a Lead Quantitative Analytics Specialist to deliver advanced quantitative analysis and modeling that generate actionable insights and support sound, evidence-based decision-making. The role will apply sophisticated analytical techniques to evaluate complex data, assess risk, validate assumptions, and identify meaningful trends and relationships.
This is an opportunity to apply deep quantitative expertise to complex business and risk challenges while contributing to the Chief Operating Office’s focus on strengthening Wells Fargo’s risk and control infrastructure, execution discipline, and operational excellence.
In this role, you will:
- Lead and contribute to moderately complex quantitative modeling and analytical initiatives, including model development, implementation, documentation, testing, and validation
- Apply statistical methods and quantitative analysis to monitor markets and assess credit and operational risk within a defined scope
- Analyze data and market developments to support short- and long-term business objectives and defined strategic initiatives
- Apply knowledge of stochastic methods, structured securities, and spread analysis to evaluate financial instruments, market behavior, and model results
- Review and assess quantitative models from technical, model-risk, audit, and market perspectives, identifying issues and recommending appropriate enhancements
- Define the structure and scope of assigned model reviews in accordance with established standards, policies, and control requirements
- Exercise independent judgment to develop, document, and evaluate analytical models that support product, marketing, risk, and business decisions
- Collaborate and consult with internal partners, model stakeholders, auditors, and regulatory-facing teams to address moderately complex model and analytical matters
- Present analytical findings, model limitations, risks, and recommendations clearly to technical and business stakeholders
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
- Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science
- 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.
- Ability to develop, analyze and optimize performance of Gen AI, Agentic AI solutions.
- Proficiency in Python-based data science and software engineering, with experience integrating GenAI into scalable, production-grade system
- 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
- Tableau, Power BI, Shiny, Dash, HTML5
- Strong oral and written communication and consultative skills
- Proactively seek opportunities to increase the value of analysis
- Ensure partner transparency throughout the life of the project
- Ensure that analyses are delivered on time, while surpassing partner expectations
- Present findings to multiple levels of management
- Consult with partners to define issues/information needs
- Find opportunities in the data and able to communicate to the partners
- Trend Analysis, forecasting and pattern recognition
- Data mining and Insights
Job Expectations:
- Ability to lead small cohort of junior analyst and provide technical guidance
- Strong collaboration skills
- 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.
Posting End Date:
29 Oct 2026*Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
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.
How we rate this
Lead Quantitative Analytics Specialist at Wells Fargo rates 19 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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Skills and AI tools this role asks for
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- How would you design a retrieval step so the model answers from real data instead of guessing?
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- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
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- List these exact terms on your resume: Prompt engineering, RAG, ML Ops, AI Evaluation, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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