Quant Developer Prod Grade -FinTrust Connect Talent Community -Remote in the U.S

Quant Developer Product Grade - FinTrust Connect Talent Community

FinTrust Connect -United States -Remote

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As a Quant Developer you will turn research into production grade code for pricing and risk. You will ship fast and safe services in Python and C++ with clean tests and CI and deliver examiner ready documentation aligned to SR 11 7 model governance. Your work may span market risk ES and VaR, xVA engines and counterparty risk, and CECL or credit scorecards depending on client needs.

Requirements:

• 5 to 10 years building production quant libraries or services in a bank or vendor
• Strong Python and C++ data structures multithreading performance profiling
• Quant methods Monte Carlo PDE regression and classification time series calibration
• Market and credit risk topics ES and VaR under FRTB, PFE, xVA CVA and DVA and FVA PnL explain
• Tooling Git unit and integration tests Docker Kubernetes Airflow Kafka or Kinesis REST and gRPC
• Data SQL KDB q or similar time series store Bloomberg or Refinitiv adapters
• Model risk awareness documentation and monitoring and controls consistent with SR 11 7 and FDIC guidance

Responsibilities:

• Productionize models turn research notebooks into tested services with APIs and SLAs
• Implement Monte Carlo and PDE pricing engines and risk measures with vectorization and optional GPU
• Build xVA components and counterparty exposure simulation with netting and collateral logic
• Deliver FRTB ES and sensitivities engines or interfaces and support desk level reporting
• Engineer reliable data feeds market data loaders static data curve and surface builders
• Create monitoring model KPIs drift stability backtesting challenger comparisons aligned to SR 11 7
• Write examiner grade docs purpose and design and assumptions and limits and tests and change logs per governance

Outcomes we track:

• Latency 30% on core risk paths within 90 days with throughput
• Test coverage 80%+ with green CI on main branch
• Model monitoring live with drift alerts and stable KPIs within 60 days
• First pass validation acceptance 95% zero r