Lead Quantitative Analytics Specialist

About this role:
Wells Fargo is seeking a Senior Quantitative Analytics Specialist. This is a partner-facing, hands-on role responsible for delivering high-impact analytics and AI/ML solutions across the end-to-end model lifecycle ranging from problem framing and model development to implementation, monitoring, and governance. The role serves as a technical subject matter expert and advisor, ensuring models are performant, explainable, and compliant with internal standards and banking regulatory expectations. This role also supports Advice & Planning capabilities by developing and validating goal-based planning, funding optimization, and simulation-based testing frameworks to improve customer outcomes under varying market conditions.
In this role, you will:

• Perform highly complex activities related to creation, implementation, and documentation
• Use highly complex statistical theory to quantify, analyze and manage markets
• Forecast losses and compute capital requirements providing insights, regarding a wide array of business initiatives
• Utilize structured securities and provide expertise on theory and mathematics behind the data
• Manage market, credit, and operational risks to forecast losses and compute capital requirements
• Participate in the discussion related to analytical strategies, modeling and forecasting methods
• Identify structure to influence global assessments, inclusive of technical, audit and market perspectives
• Collaborate and consult with regulators, auditors and individuals that are technically oriented and have excellent communication skills

Required Qualifications:

• 4+ 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 programming and data skills: Python, PySpark, SQL; experience working with large datasets.
• Solid ML/statistical foundation: regression (linear/logistic), time series, multivariate analysis; tree/ensemble methods (RF, XGBoost/GBM), SVM; and practical understanding of model evaluation and tuning (e.g., AUC/ROC).
• Strong applied quantitative modeling background, including optimization and/or simulation techniques used in planning, allocation, or decisioning problems.
• Hands-on experience implementing optimization models (linear programming preferred) and translating objective functions and constraints into production-ready code.
• Solid understanding of uncertainty modeling and simulation (e.g., Monte Carlo), including summarizing distributional outcomes and stress/adverse-condition analysis.
• Experience in model deployment, UAT support, and model monitoring/maintenance in production.
• Strong analytical problem-solving and critical thinking; ability to learn business context quickly and collaborate across teams.
• Proven hands-on experience building and deploying ML solutions on GCP, with strong proficiency in BigQuery and Vertex AI (training and operationalization).
• Familiarity with Generative AI concepts (LLMs, embeddings, prompt-based systems) for awareness and future collaboration.
• Hands-on exposure to GenAI application prototyping and evaluation (e.g., prompting, retrieval concepts, embeddings/vector search, basic guardrails).
• Understanding of Responsible AI principles (fairness, explainability, data privacy) relevant to both traditional ML and emerging AI approaches.
• Deep learning exposure (ANN/RNN/CNN/LSTM) and/or frameworks such as TensorFlow/Keras/PyTorch.
• Big data ecosystem experience (Spark/Hadoop/H2O/Teradata/Aster) and experience optimizing pipelines with Data Engineering partners.
• Experience with stochastic process modeling relevant to markets (e.g., interest rates, inflation, correlations) and applying these in simulation frameworks.
• Experience building scalable simulation orchestration (parallelized runs; scenario/sampling frameworks; group-based experimentation).
• Familiarity with goal-based planning/advice engines: goal setup, contribution recommendations, funding source selection, monitoring for material changes, and personalized action generation.
• Banking domain familiarity (e.g., deposits, loans, cards, mortgage, wealth) and/or functional areas (risk, marketing, operations).
• Experience with end-to-end deployment practices including packaging, CI/CD integration, and production monitoring; familiarity with model lifecycle tooling such as experiment tracking, model registry, and automated drift/performance monitoring.

Job Expectations:

• Lead end-to-end AI/ML initiatives: data analysis, feature engineering, model development, validation, performance testing, implementation, and monitoring.
• Build prototypes quickly to validate feasibility and business value; evolve prototypes into scalable, reusable solutions.
• Partner with business stakeholders to define problems, align success metrics, and deliver executive-ready insights and recommendations.
• Collaborate with technology and engineering teams to productionize models; support UAT, deployment, and operational readiness.
• Ensure strong model governance: documentation, change management, audit readiness, and adherence to applicable model risk expectations and internal controls.
• Review/validate models and provide guidance to improve performance while meeting regulatory and internal control requirements.
• Mentor junior data scientists and contribute to CoE best practices, standards, and reusable assets.
• Build and enhance goal-based financial planning models translating customer goals, balances, liabilities, and budget into actionable funding plans and recommended actions.
• Develop and maintain optimization-based models (e.g., linear programming) that allocate contributions/withdrawals and reallocate assets/liabilities to maximize goal completion subject to constraints.
• Design and execute Monte Carlo / stochastic simulation frameworks to test planning strategies under market uncertainty and generate distributional outcomes (e.g., medians, confidence intervals, adverse-case performance).

Posting End Date:
23 Apr 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 .
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Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
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b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.