Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, DTCC, is seeking the following. Apply via Dice today! Are you ready to make an impact at DTCC? Do you want to work on innovative projects, collaborate with a dynamic and supportive team, and receive investment in your professional development? At DTCC, we are at the forefront of innovation in the financial markets. We are committed to helping our employees grow and succeed. We believe that you have the skills and drive to make a real impact. We foster a thriving internal community and are committed to creating a workplace that looks like the world that we serve. The Information Technology group delivers secure, reliable technology solutions that enable DTCC to be the trusted infrastructure of the global capital markets. The team delivers high-quality information through activities that include development of essential, building infrastructure capabilities to meet client needs and implementing data standards and governance. Pay and Benefits: Competitive compensation, including base pay and annual incentive Comprehensive health and life insurance and well-being benefits, based on location Pension / Retirement benefits Paid Time Off and Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being. DTCC offers a flexible/hybrid model of 3 days onsite and 2 days remote (onsite Tuesdays, Wednesdays and a third day unique to each team or employee). The Impact You Will Have in This Role As a Quantitative Software Engineer , you will play a key role in designing and delivering production-grade Python solutions that support critical quantitative risk initiatives , that strengthen the Financial Risk Management posture of DTCC. This role sits at the intersection of software engineering, data engineering, and applied statistics , translating complex mathematical and statistical models into scalable, maintainable systems used in real-world financial decision-making. You will work closely with a globally distributed quantitative risk team , contributing solutions that are reliable, performant, and production-ready. Your Primary Responsibilities Implement and productionize quantitative, statistical, and mathematical models using Python Engineer and maintain scalable solutions that run reliably in production environments Support quantitative risk model implementations to support the Financial Risk Management department Design, build, and maintain data pipelines leveraging Python, Snowflake, and relational databases Translate existing analytical, statistical, or theoretical models into clean, maintainable Python code Own solutions end - to - end, from model implementation through deployment, monitoring, and production support Collaborate closely with quantitative analysts, risk partners, and global stakeholders Provide L3 production support on an as - needed basis via PagerDuty (issue - based coverage; no fixed on - call rotation) Contribute within an Agile development environment, following strong engineering and control practices Ensure solutions meet standards for performance, reliability, documentation, and operational controls Qualifications Bachelor's degree preferred or equivalent practical experience Minimum of 6 years of related experience Talent Needed for Success Strong experience in Python software development for analytical model implementation, building production - grade code (beyond scripting or notebooks) Hands - on experience working with quantitative, statistical, or mathematical models Experience with Snowflake or similar cloud - based data platforms Solid foundation in statistics, data analysis, or quantitative concepts Background in financial services, risk management, capital markets, or related domains Experience working within an Agile delivery model Strong communication skills and ability to collaborate across global teams Experience supporting production systems, including L3 support when required Nice To Have Skills Experience implementing or productionizing risk, capital, or liquidity models Exposure to AI / machine learning concepts (baseline understanding; this is not a data scientist or research role) Prior experience supporting quantitative risk management teams Experience converting academic or theoretical models into reliable production software The salary