Description

You will validate and migrate machine learning models from Java to Python, ensuring metric consistency and operational readiness.

This role is remote.

Responsibilities

• Interpret data transformation logic and validate feature pipelines from existing Java implementations.
• Run Python-converted models on historical datasets and validate output metrics against Java model benchmarks.
• Collaborate with model validation teams to review performance, consistency, and explain metric deviations.
• Design 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 reproduce training and scoring workflows.

Required Skills

• 8+ years of hands-on experience with Python for machine learning.
• Expertise in XGBoost, Scikit-learn, NumPy, and Pandas.
• Proficiency in PySpark for reading, transforming, and analyzing large datasets stored in parquet.
• Experience validating or reverse engineering ML models from business logic or legacy implementation.
• Familiarity with Java-based ML libraries or understanding of cross-language internals.
• Experience with Python frameworks for meta-modelling libraries.
• Ability to understand the logic, structure, and parameters of Java-based XGBoost models.

Key Skills

Education

Any Graduate