Machine Learning Engineer

Hiring: W2 Candidates Only

 Visa: Open to any visa type with valid work authorization in the USA

Key Responsibilities:

• Collaborate with the customer team to thoroughly understand the logic, structure, and parameters of the existing Java-based XGBoost models.
• Interpret data transformation logic and validate feature pipelines from the existing Java implementations.
• Execute the Python-converted models on historical datasets and validate output metrics against Java model benchmarks.
• Work closely with model validation teams to review performance, consistency, and explain any metric deviations.
• Design and implement unit tests and validation scenarios to support each migrated model's readiness for signoff.
• Ingest model input data from Parquet files using PySpark and pandas to accurately reproduce training and scoring workflows.
• Conduct Exploratory Data Analysis (EDA) and spot-check row-level predictions where necessary.

Essential Skills:

• 10+ years of hands-on experience with Python for Machine Learning, specifically with libraries like XGBoost, scikit-learn, and NumPy/pandas.
• Proficiency in using PySpark for reading, transforming, and analyzing large datasets stored in Parquet files.
• Extensive experience in validating or reverse engineering ML models from complex business logic or legacy implementations.
• Exposure to Java-based ML libraries (like DL4J) or a strong understanding of how internal model components map across different programming languages.

Desirable Skills:

• Hands-on experience with Python frameworks for meta-modeling libraries.
• Prior experience in a financial or regulated environment.
• Keywords in your resume: Data Scientist, Machine Learning Engineer, XGBoost, Python, PySpark, Java, Model Migration, DL4J, Data Validation, scikit-learn, pandas, NumPy, Metric Parity, Parquet, EDA,