Quantitative ML Engineer

Job Title: Quantitative ML Engineer (PyTorch & PPNR Migration)
Job Location: New York (Hybrid)
Job Type: Long-term Contract

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 for modularity and custom Autograd functions where necessary.

Optimization: Refactor code to leverage Databricks distributed computing and PyTorchs 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.