About this role:
Wells Fargo is seeking a Quantitative Software Engineer, Executive Director (Senior Lead Securities Quantitative Analytics Specialist). A successful applicant will be a Python quantitative developer in the Investment Portfolio in Wells Fargo Securities, with a focus on Juniper Vasara ALM development.
Vasara is the next generation risk platform for the bank. It is an ambitious, green field initiative to tackle the bank's risk computation challenges within capital markets, from ticking risk for trading desks to market risk and capital calculations such as FRTB and CCAR.
Juniper Vasara is a joint venture between multiple Quant and Technology teams, and you will work as an ALM quant developer focusing on specific risk management and balance sheet strategy solutions for our portfolio management partners. Juniper Vasara is a horizontal solution designed to be use case agnostic to achieve maximum consistency and re-usability.
Essential duties and responsibilities include:
- Implement ALM models and logic in Python.
- Integration of pricing and risk analytics in collaboration with other quant teams, providing expertise in design and implementation issues relate to ALM.
- Effective communication and collaboration with Business Stakeholders, other Quant Teams, Technology Partners, and Project Management
- Analyze performance, propose remedial or optimization plans, and ensure execution to enhance the new strategic valuation and risk platform for the securities businesses
- Consistently deliver high-quality software and documentation in an Agile SDLC
In this role you will:
- Proactively participate in complex software design & development activities within an Agile environment
- Contribute to large-scale project planning, balancing short and long-term objectives
- Generate, test, implement, and deploy ideas to improve system performance or team productivity.
- Use quantitative and technological techniques to solve complex business problems
- Meet deliverables while leveraging solid understanding of policies, procedures, and compliance requirements
- Collaborate and consult with peers, colleagues, and project managers to resolve issues and achieve goals
- Effectively communicate with and build consensus with all project stakeholders
Required Qualifications:
- 7+ years of Securities Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 7+ years of professional Python 3 experience
Job Expectations:
- Ability to travel up to 10% of the time
- This position is eligible for Visa sponsorship
- Must be able to work on-site
Desired Qualifications:
- 5+ years of hands-on coding experience, Python and C++ are most relevant
- 3+ years of product and market experience in various asset classes: rates, foreign exchange, credit, and mortgages, and structured products
- 3+ years of quantitative analytics library software development experience in a buy-side or sell-side institution or a quant solution vendor
- 1+ year hands on experience with ALM frameworks or prior experience in ALM vendor software
- Strong proficiency in Python is encouraged.
- The following items are considered bonus qualifications:
- Understanding of advanced language features, runtime behavior, and performance characteristics
- Deep understanding of CPython internals (interpreter loop, memory model, GIL, garbage collection); experience customizing or extending the Python interpreter or standard library is a plus
- Proven experience with Python interoperability and bindings to lower-level languages (e.g., C/C++, Cython, pybind11, Java)
- Hands-on experience writing high-performance GPU code using Python-based DSLs (e.g., Triton, CUDA Python, JAX/XLA kernels)
- Demonstrated expertise in Python performance optimization across CPU architectures using tools such as Numba, Cython, vectorization, and profiling techniques
- Experience designing and developing large-scale, distributed, high-performance Python systems in production
- Strong understanding of multithreading and multiprocessing in Python, including practical strategies for working with or around the GIL in real-world production environments
- Experience operating Python systems under strict latency, throughput, or reliability constraints (e.g., low-latency trading systems, ML inference serving, large-scale data pipelines)
- Experience interpreting and solutioning for risk
- Master's degree or higher in computer science or finance/mathematics
- Experience in software development cycle and agile technologies, e.g. Git, Jira, Confluence
- Experience in or passionate about Agentic AI
- Excellent verbal, written, and interpersonal communication skills