JPMorganChase
Quant Modeling Lead - Python

How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Job Description
About the Role
As a Python Developer within the Wholesale Credit Quantitative Research Core team, you will play a central role in building and maintaining Nova – the firm’s strategic platform for Loan Loss Forecasting models. Nova is built within JPMorgan’s Athena platform and underpins critical regulatory and business processes including CECL, IFRS 9, CCAR, ICAAP, and Risk Appetite forecasting.
You will be responsible for designing and implementing the core frameworks and libraries that model developers rely on to build, test, and deploy forecasting models at scale. This is a hands-on engineering role that demands strong software craftsmanship, quantitative aptitude, and the ability to translate partially defined business needs into robust, production-quality systems. You will work closely with quantitative researchers, model governance, technology partners, and wholesale credit business stakeholders.
Job Responsibilities
Design, build, and maintain the core Python frameworks and libraries that power Nova, ensuring they are performant, extensible, and easy for model developers to integrate with Develop and enhance the calculation engine and related tooling for loan loss forecasting models, supporting CECL, IFRS 9, CCAR, ICAAP, and Risk Appetite requirements Implement high-performance numerical algorithms using Python scientific computing libraries including NumPy, Pandas, and DuckDB Champion test-driven development practices across the team, building and maintaining comprehensive unit, integration, and regression test suites to ensure framework reliability Take partially specified problems and business needs from stakeholders and translate them into concrete technical requirements, designs, and implementation plans Leverage LLM-based coding tools (e.g., GitHub Copilot, Claude) to accelerate development velocity, drive code quality, and maximize team productivity Perform peer code reviews with a focus on correctness, performance, maintainability, and adherence to team standards Prepare clear and thorough technical documentation covering design decisions, implementation details, and testing strategies Partner with model developers, product and business stakeholders during the implementation, testing, and operationalization of forecasting processes Present regular updates on development progress, technical decisions, and platform roadmap to senior management and cross-functional stakeholders Investigate and debug counter-intuitive observations in model forecasts, performing root-cause analysis at the framework and data level
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Required Qualifications, Capabilities, And Skills
Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative discipline Minimum 5 years of experience in quantitative software development within a financial services environment (e.g., banking, asset management, hedge fund, fintech) Advanced proficiency in Python with deep experience in object-oriented design, design patterns, and building production-grade frameworks and libraries Strong working knowledge of NumPy and Pandas for numerical computing and data manipulation Demonstrated experience with test-driven development and building systems with rigorous unit, integration, and regression test coverage (e.g., pytest, unittest) Strong analytical, quantitative, and problem-solving skills with the ability to reason about complex model behavior and data flows Excellent written and verbal communication skills, with the confidence to present technical concepts to both technical and non-technical audiences Proven ability to operate as a self-starter: taking ambiguous or partially specified problems and driving them through to well-defined technical solutions Proficiency with LLM-based coding tools and a track record of leveraging AI assistants to meaningfully increase development productivity and code quality
Preferred Qualifications, Capabilities, And Skills
Experience with DuckDB or similar in-process analytical databases for high-performance data querying and transformation Knowledge of credit risk concepts including Wholesale Credit, CCAR/DFAST stress testing, CECL/IFRS 9 allowance, and Basel III regulatory capital Experience working with or building upon large-scale analytics platforms (e.g., JPMorgan Athena or comparable quantitative computing environments) Familiarity with distributed computing frameworks and techniques for scaling numerical workloads Knowledge of statistical modeling, Monte Carlo simulation, and time-series forecasting methodologies Ability to work effectively with large datasets and practical knowledge of SQL and database systems Proven ability to build collaborative relationships with cross-functional partners including model developers, business stakeholders, and technology teams


Get help with your application
Your very own career expert that helps elevate your application to the next level.
About Us
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About The Team
J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location