Чтобы адаптировать резюме под вакансию или составить сопроводительное письмо, загрузите резюме
описание
Citi is a banking and financial services company. Its Risk Technology team is developing AI-enabled automation across the quantitative model lifecycle for market risk and credit risk models.
задачи
Design, develop, and maintain Python-based services, pipelines, and tools for quantitative model lifecycle automation across market risk and credit risk model families;
Build and operate automated testing and validation frameworks for quantitative models, including test orchestration, result capture, benchmarking, and reporting;
Develop data analysis and reconciliation tooling for large-scale risk datasets, ensuring data quality, lineage, and traceability across risk platforms;
Contribute to model lifecycle management tooling covering model inventory, workflow orchestration, approvals, periodic reviews, and audit-ready evidence generation;
Implement AI/ML components in model workflows, including LLM-based model documentation automation, intelligent data quality checks, and workflow assistance;
Integrate AI tooling into controlled, auditable, production-grade environments with testing, monitoring, and governance controls;
Stay current with applied AI/ML developments and evaluate their applicability to risk technology use cases;
Work within a cross-functional agile team alongside quants, validators, data engineers, and program management;
Promote engineering best practices in code quality, testing, CI/CD, documentation, and secure, scalable design;
Mentor junior developers and contribute to technical design reviews.
требования
Hold a STEM degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field;
Have professional software development experience with deep expertise in Python and its data and engineering ecosystem, including pandas, NumPy, FastAPI, and orchestration tools;
Have delivered production-grade automation, data pipelines, or testing frameworks in a complex enterprise environment;
Have solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM-based application development;
Have strong experience with test automation, CI/CD, Git, code review, containerization, and modern software engineering practices;
Have experience with large datasets, data quality and lineage checks, SQL, and enterprise data platforms;
Communicate technical concepts effectively to non-technical stakeholders and work effectively in a cross-functional, global team;
Nice to have: Master's degree, financial services experience, exposure to quantitative risk models and their lifecycle, familiarity with model risk regulation and banking governance, workflow orchestration platforms, AWS or Google Cloud, Docker, Kubernetes, quantitative finance, statistics, data science, an advanced quantitative degree, experience mentoring engineers, and experience leading small technical workstreams.
.svg)
