Data Scientist

Role Overview Technically strong and detail-oriented Data Scientist to support the model development and migration in Java as well as in Python migrate and validate XGBoost based ML models originally developed in Java (based on DL4J library) into Python using a custom-built internal Python framework. Work closely with PySpark engineers, platform teams and validation teams to ensure metric parity. Key Responsibilities Collaborate with the customer team to understand the logic, structure and parameters of the Java-based XGBoost models 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 models 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 Skills Required 7-10 years hands-on with Python for machine learning especially XGBoost, scikit-learn and NumPypandas 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