Quantitative Analytics Specialist

About this role: Wells Fargo is seeking a Quantitative Analytics Specialist. The 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. In this role, you will: Develop, implement, and calibrate various analytical models Perform highly complex activities related to financial products, business analysis and modeling Perform basic statistical and mathematical models using Python, R, SAS, C++ and SQL Perform analytical support and provide insights regarding a wide array of business initiatives Provide solutions to business needs and analyze workflow processes to make recommendations for process improvement in risk management Collaborate and consult with peers, colleagues, managers, and regulators to resolve issues and achieve goals Required Qualifications: 2+ 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 statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline Desired Qualifications: 2+ years of hands-on experience in AI/ML model development and implementation in applied business settings. 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 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. Job Expectations: Lead the development and application of causal