Responsibilities:

• Own, maintain, and optimize an existing production-grade predictive model built using CatBoost and standard Python ML libraries.
• Develop robust data preprocessing pipelines and feature engineering strategies to improve model accuracy using large, historical datasets from job tracking applications.
• Implement rigorous model validation techniques, track performance metrics (e.g., RMSE, MAE), and proactively identify areas for improvement.
• Collaborate directly with Data Engineers, Software Developers, and Product Managers to integrate and serve ML models within production systems.
• Analyze model output, investigate anomalies, and ensure overall model reliability and reproducibility.
• Clearly document your work and communicate complex model behavior and findings to both technical and non-technical stakeholders.
• Explore and prototype advanced modeling techniques (e.g., Bayesian models, statistical methods) for future product enhancements.

Must-Have Skills:

• Expert-level proficiency in Python for machine learning and data analysis, with proven professional experience.
• Deep, hands-on expertise with scikit-learn, pandas, NumPy, and standard data preprocessing libraries.
• Practical, professional experience with CatBoost (or similar gradient boosting frameworks like XGBoost or LightGBM), including model training, validation, and hyperparameter tuning.
• Solid understanding of end-to-end ML lifecycle: feature engineering, model training, validation, hyperparameter tuning, and performance measurement.
• Proven ability to clean, analyze, and derive insights from large, structured, real-world datasets.
• Professional experience with Jupyter Notebooks and Git for version control.
• Excellent problem-solving skills and the ability to communicate complex concepts clearly and effectively.

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