Quantitative ML Engineer

Hybrid in New York, NY, US • Posted 1 day ago • Updated 1 day ago

Contract W2

Hybrid

$60 - $70/hr

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Job Details

Skills

ML Engineer
PPNR
C++/R
Databricks and PyTorch

Summary

Position Details:

Quantitative ML Engineer (PyTorch & PPNR Migration)

New York (Hybrid)

Experience: 6-8 Years

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 Data Loaders.
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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91172961
Position Id: 8911608
Posted 1 day ago

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Job Title: Quantitative ML Engineer (PyTorch & PPNR Migration) Location: New York, NY (Hybrid) Duration/Term: Long Term Contract Job Description We are seeking an experienced Quantitative ML Engineer to lead the migration of complex PPNR (Pre-Provision Net Revenue) forecasting models from legacy Hadoop/C++/R environments to a modern Databricks and PyTorch ecosystem. The role involves translating intricate econometric and statistical modeling logic into optimized PyTorch tensor-based implementati

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