Wells Fargo & Company is a nationwide, diversified, community-based financial services company that is headquartered in San Francisco with major locations around the country. Founded in 1852, Wells Fargo is one of the country’s oldest and most stable companies. We have more than 265,000 team members and serve about one in three households in the United States.
We are looking for talented and ambitious individuals to join our large community of quants who are working on a wide range of problems in model development, model risk assessment, and audit. Areas of applications include: loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and stress testing. We use state-of-the-art statistical, mathematical, and machine learning techniques to develop and assess models in these areas. We also use AI techniques (natural language processing, deep learning algorithms, and others) to model information in unstructured data (text, voice and images).
Corporate Model Risk (CMoR):
CMoR is Wells Fargo’s center of excellence for validating all models in the enterprise. Validation involves assessing all aspects of model risk: data quality and bias, replication development models, assessing their assumptions and limitations, and developing benchmark models to challenge performance, interpretability, and so on. CMoR has more than 150 quants and data scientists who validate a variety of models in credit and operational risks as well as capital markets. We are looking for talented and ambitious individuals to join our community of quants. We provide an exciting and diverse environment where you’ll have the ability to work on interesting and challenging problems, using traditional as well as cutting-edge techniques to assess model risks. You’ll also have the opportunity to use your problem-solving, organizational and communications skills to build your career.
The Advanced Computing and AI Engineering Team within the Advanced Technologies for Modeling (AToM) Group in CMoR has openings for qualified candidates. The person will be working at the intersection of Data Science & AI/ML Research, Technology, and Quantitative Modeling. The responsibilities include, but are not limited to, the following:
Helping to develop the architecture and foundational components of our model risk platform, developing fully operational prototypes, and deploying them in partnership with our Technology partners.
Working on one or more projects dealing with distributed model training and optimization, intelligent data pipelines, model integration and continuous backtesting, automated model benchmarking, and specialized data visualization.
Contributing to the design and evolution of CMoR’s computing infrastructure: distributed computing clusters with a traditional big data stack (Spark/HDFS/Hive etc.), and GPU clusters for specialized AI/ML model training and GPU-enabled simulation.
Providing guidance to the broader CMoR community on best engineering practices to run modeling and analysis at scale.
A PhD in statistics, mathematics, physics, engineering, computer science, economics, or quantitative field; or a Master s degree in the above areas with 2+ years of experience in one or a combination of the previously mentioned fields above
Other Desired Qualifications
At least four years of hands-on experience and deep knowledge in at least one of these areas: data engineering and/or compute-intensive systems, with a track record of success in delivering systems into production.
Change management skills and proven ability to conceptualize and communicate designs and plans;
Knowledge of recent frameworks for building Machine Learning pipelines such as MLFlow, Kubeflow, Airflow, and TFX;
Knowledge of typical big data stacks, with in depth knowledge of at least one component – e.g. Spark, Kafka, Impala;
Knowledge of common machine learning frameworks: e.g. scikit-learn, Tensorflow, PyTorch;
Good understanding of service-oriented architectures, microservices, modern RPC and message buses;
Hands-on experience with traditional high performance and scientific computing: schedulers (e.g. SLURM), MPI, OpenMP;
Excellent programming skills in a systems language (e.g. C, C++, Java, Scala, Go), and one specialized language (Python, R, Julia);
Deep understanding of data modeling and relational databases. Advanced SQL, including analytical functions and recent additions to the SQL standard;
Knowledge of common data visualization toolkits (e.g. D3, Vega,) and frontend analytical applications. Knowledge of React is a big plus;
Good verbal and written communication skills as well as interpersonal skills;
Ability to prioritize work, meet deadlines, achieve goals, and work in a dynamic and complex environment; and
Ability to develop partnerships and collaborate with other business and functional areas.
All offers for employment with Wells Fargo are contingent upon the candidate having successfully completed a criminal background check. Wells Fargo will consider qualified candidates with criminal histories in a manner consistent with the requirements of applicable local, state and Federal law, including Section 19 of the Federal Deposit Insurance Act.
Relevant military experience is considered for veterans and transitioning service men and women.
Wells Fargo is an Affirmative Action and Equal Opportunity Employer, Minority/Female/Disabled/Veteran/Gender Identity/Sexual Orientation.