Quantitative ML Engineer (PyTorch & PPNR Migration) (New York)

Job Title: Quantitative ML Engineer (PyTorch & PPNR Migration) Location: New York City, NY (Onsite-Hybrid) Term: Contact

Role Objective We are looking for a Quantitative ML Engineer to lead the technical migration of complex PPNR (Pre-Provision Net Revenue) forecasting models from a Hadoop/C++/R environment to a modern Databricks and PyTorch ecosystem. You will be responsible for translating legacy mathematical logic into optimized PyTorch tensors while ensuring strict numerical parity required for US regulatory compliance (CCAR/DFAST).

Key Responsibilities Model Translation: Reverse-engineer legacy C++ and R codebases to extract core mathematical logic, econometric formulas, and simulation parameters. PyTorch Implementation: Re-implement these models in PyTorch, utilizing advanced features like torch.nn for modularity and custom Autograd functions where necessary. Optimization: Refactor code to leverage Databricks’ distributed computing and PyTorch’s GPU/parallel processing capabilities to reduce model execution time. Data Integration: Build high-performance pipelines from Snowflake into Databricks using Spark and PyTorch DataLoaders. Parity & Validation: Conduct rigorous back-testing and sensitivity analysis to ensure the new PyTorch models yield results statistically identical to the legacy Hadoop outputs. Regulatory Documentation: Collaborating with Model Risk Management (MRM) to document the migration process, architectural changes, and validation results in compliance with SR 11-7 standards. Required Technical Skills Frameworks: Expert-level PyTorch (specifically for non-computer vision tasks like time-series, regression, or Monte Carlo simulations). Languages: High proficiency in Python and a strong ability to read and interpret C++ and R (specifically statistical packages like lme4 or forecast). Platforms: Hands-on experience with Databricks (MLflow, Spark) and Snowflake (Snowpark is a plus). Quantitative Finance: Deep understanding of statistical modeling, econometric forecasting, or financial risk management. Big Data: Experience migrating workloads out of Hadoop/Hive environments. Preferred Qualifications Experience specifically with PPNR, CCAR, or DFAST regulatory modeling. Masters or PhD in a quantitative field (Statistics, Financial Engineering, Physics, or Math). Experience with TorchScript or ONNX for model productionisation.