Data Scientist

10 years hands-on with Python for machine learning especially XGBoost, scikit-learn and NumPy/pandas.

• Proficiency in PySpark for reading, transforming and analyzing large datasets stored in parquet.
• Experience in validating or reverse engineering ML models from business logic or legacy implementation.
• Exposure to Java-based ML libraries or understanding of how internals map across languages.
• Hands-on with Python frameworks for meta-modelling libraries.

Roles & 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 if any.
• 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.
• Conduct EDA and spot-check row-level predictions where needed Collaborate with the customer team to
• understand the logic, structure and parameters of the Java-based XGBoost models.