Skills

Python Big Data Risk Management PyTorch Computer Vision Regression C++ Snowflake Hadoop Spark Databricks

Job Specifications

Job Title: Quantitative ML Engineer/Data Scientist (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.
Master’s or PhD in a quantitative field (Statistics, Financial Engineering, Physics, or Math).
Experience with Torch Script or ONNX for model productionisation.

About the Company

Net2Source (N2S) is a global workforce solutions company recognized by SIA as the largest and fastest-growing Total Talent Solutions provider with a presence in 32 countries. and in-house Glo-Cal (global and local) teams to support our clients. We carve out custom talent solutions, keeping People, Process, and Technology as the pillars of making the process simple, robust, and efficient. With over 3,500+ contractors working worldwide, we specialize in Contingent Staffing, RPO, Direct Sourcing, Payroll Solutions (EOR/AOR), ... Know more