Quantitative Analytics Specialist

About this role:

Wells Fargo is seeking a Quantitative Analytics Specialist

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
• Master''s degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline

Desired Qualifications:

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related field.
• Experience in machine learning, predictive modeling, or quantitative analytics, preferably within financial services.
• Strong programming skills in Python with hands-on experience using PySpark for large-scale data processing.
• Solid understanding of statistical and machine learning concepts (e.g. supervised and unsupervised learning, time-series analysis, graph) and deep understanding of model evaluation
• Demonstrated ability to work independently, take ownership of projects, and manage multiple stakeholders across technical and business teams
• Excellent communication and storytelling skills, especially in translating technical outcomes to business insights
• Experience working in a regulated environment (e.g., banking, insurance, fintech) with exposure to model risk governance or regulatory modeling practices.
• Exposure to Generative AI concepts (e.g., LLMs, transformers, prompt engineering) and experience in building or experimenting with generative and agentic solutions.
• Familiarity with AI frameworks (e.g. Langchain, Langgraph, PyTorch) for business applications.
• Familiarity with MLOps and LLMOps tools and best practices of model orchestration
• Knowledge of quantitative finance, portfolio modeling, or risk management is a plus
• Experience in cloud environments (e.g., AWS, Azure, GCP) and containerized deployments (e.g., Docker, Kubernetes) is a bonus

Job Expectations:

• Design, build, and deploy machine learning and predictive AI models to solve complex business problems in banking and financial domains (e.g., Digital customer experience, fraud detection, forecasting)
• Collaborate closely with quantitative analysts and business stakeholders to understand requirements, develop solutions, and present insights.
• Contribute to GenAI-based proof-of-concepts and experimentation, exploring how generative techniques can enhance decision-making or automation in banking use cases.
• Write efficient, production-grade code using Python and PySpark, and ensure scalability and robustness in a distributed computing environment
• Identify automation opportunities; standardize model pipelines and controls (versioning, testing, deployment)
• Apply rigorous testing and validation strategies to ensure models are explainable, auditable, and compliant with internal risk/governance standards.
• Collaborate with data engineers, model validators, and product managers to ensure timely delivery and alignment with enterprise data architecture.
• Show strong project ownership, proactively driving tasks, managing timelines, and communicating blockers or dependencies effectively.