Quantitative Machine Learning Engineer

Job Title : Quantitative Machine Learning Engineer – PPNR Model Migration (PyTorch & Databricks) Location : New York City , NY -Hybrid Duration : 12+ Months 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.