Lead Quantitative Analytics Specialist

About this role:
Wells Fargo is seeking a Lead Quantitative Analytics Specialist...
MRM operates in a fast-paced work environment with continuously changing policies and technologies. The successful candidate is expected to be self- motivated, require minimal supervision, and produce work that is consistent with MRM's recognized high standards. Effective work will involve familiarity with source systems of record, analytical data and sampling plans, model replications, model performance assessments, and test model development as effective challenges to lines of business. It further requires strength in writing detailed standard analytical reports to ensure Wells Fargo's compliance with governance policies and regulations. Each validation report will include assessments of the specific business, model purpose and history, the model methodology, data integrity, model development, performance, implementation, and monitoring. These documents are read by a broad audience, including auditors and regulators.
In this role, you will:

• Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense of highly statistical theory
• Qualify monitor markets and forecast credit and operational risks
• Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives
• Utilize stochastic, structured securities, spread analysis, with the expertise in the theory and mathematics behind the analysis
• Review and assess models inclusive of technical, audit, and market perspectives
• Identify structure and scope of review
• Enable decision making for product and marketing with broad impact and act as key participant to develop and document analytical models
• Collaborate and consult with regulators and auditors
• Present results of analysis and strategies

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

Job Expectations:

• Perform as an individual contributor role in the Decision Science and Artificial Intelligence (DSAI) Validation team within MRM.
• In this role, the team member is responsible for validating and approving all Retail Credit scoring, Commercial Credit scoring, Financial crimes, Fair lending, Loss forecasting and other artificial intelligence and machine-learning models.
• A Lead Quantitative Analytics Specialist should have a deep academic knowledge, broad based approach to solutioning business problems. He/she should approach the problem agnostic of analytic technique, tool or process. Ability to think outside the box and provide ensemble solutions should set them apart to be a high performing team member.
• Develop, train, validate machine learning model for Credit Scoring and Risk management
• Perform highly complex activities related to creation, implementation, and documentation
• Use highly complex statistical theory to quantify, analyze and manage markets
• 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
• Mentor and support junior team members, sharing best practices and technical expertise
• Lead technical strategy and guide analytical direction within the team
• Implement novel architecture, including Agentic AI, Gen AI and LLMs
• Present results of analysis and strategies

Desired Skills:

• Hands on industry experience in building large scale end-to-end machine learning systems using a combination of big data tools and programming languages like Scala, Java and/or Python
• Knowledge of parallel and distributed computing frameworks such as Hadoop, Spark, MPI, using GPUs
• Experience in machine learning areas such as recommender systems, NLP, pattern recognition, predictive modeling, Artificial Intelligence.
• Experience implementing machine learning algorithms such as support vector machines, decision trees, logistic regression, clustering, neural networks, graphical models etc
• Data exploration and preparation using SAS, Spark, Python ,SQL or R or any statistical tool
• Working experience in financial model validation team would be added advantage
• Prepare detailed documentations for projects for both internal and external that complies regulatory and internal audit requirements
• Keep updated with the latest in the Data Science community and leverage new capability for the bank
• Knowledge of banking industry, terminologies and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
• Knowledge of any functional area such as risk, marketing, operations or supply chain in banking industry.
• Instrumental in bringing new approaches to the table, create white papers and present in conferences
• 5+ years of experience with minimum Masters/Phd in a quantitative field such as applied math, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.
• Strong mathematical, statistical, analytical and computational skills.
• Strong communication skills for a variety of audiences (other technical staff, senior management and regulators) both verbally and in writing.
• Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions.
• Ability to work independently on complex model validations from start to finish.
• Skill in managing relationships with key model stakeholders
• Eagerness to contribute collaboratively on projects and discussions
• Perpetual interest in learning something new, but being comfortable with not knowing the all the answers
• Attention to detail in both analytics and documentation
• Aptitude for synthesizing data to 'form a story' and align information to contrast/compare to industry perspective
• Intellectually curious, who enjoy solving problems.