Hi,
have come across your profile on one of the job boards or in our database and would like to explore your interest and availability for a contract position with our client - a leader in its industry. If you are interested in pursuing this opportunity please reply to this email with your resume and expected compensation.
You can also search all the open jobs with VARITE and apply online at: (https://www.varite.com/careers/).
Job Title: Lead ML Engineer (PyTorch & PPNR Migration)
Location: New York, NY
Duration: 48 weeks
Work Mode: Hybrid
Pay range: $75-78/hr on C2C //No OPT and GC profiles pls
top skills: PyTorch/Databricks/ML flow/Python
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.
Thanks & Regards,
Kandula Subba Reddy | Team Lead – Delivery
VARITE, INC
1871 The Alameda, Suite 120, San Jose, CA 95126
Ph: 913-583-5594
E: subba.reddy@varite.com | www.varite.com | Careers @ VARITE
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