Senior Quantitative Analytics Specialist

Wells Fargo is seeking a Senior Quantitative Analytics Specialist. The Senior Quantitative Analytics Specialist 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 Causal Inference capabilities by developing and validating ML models to understand the impact of business decisions.

• 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

• 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

• 4 years of hands-on experience in AI/ML model development and implementation in applied business settings.
• Strong experience developing and validating causal inference models to estimate treatment effects and measure business impact.
• Hands-on expertise with causal machine learning techniques, including T-Learners, S-Learners, X-Learners, Doubly Robust Learners, Causal Forests, Uplift Modeling, and KNN-based approaches.
• Experience with propensity score matching/weighting, inverse probability weighting (IPW), difference-in-differences (DiD), synthetic control methods, regression discontinuity, and instrumental variable techniques.
• Proficiency in designing and analyzing A/B tests, quasi-experiments, and observational studies.
• Strong knowledge of counterfactual analysis, treatment effect estimation (ATE, ATT, CATE), confounding bias mitigation, and model interpretability.
• Ability to translate causal insights into actionable business recommendations and communicate findings effectively to technical and non-technical stakeholders.
• Strong foundation in statistics, machine learning, experimental design, and large-scale data analysis.
• 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.

• Lead the development and application of causal inference methodologies to measure the impact of business actions, generate actionable insights, and support strategic decision-making.
• Collaborate across teams to design experiments, deploy scalable solutions, and communicate findings to stakeholders and leadership.
• Translate complex causal findings into clear recommendations for senior leadership and non-technical stakeholders.
• Collaborate with data engineers, data scientists, product teams, and business partners to operationalize causal models in production.
• Stay current with advancements in causal AI, experimentation, and machine learning, and drive adoption of best practices within the team.
• Mentor junior team members and contribute to the development of the organization's causal inference capabilities.