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Popular senior ML role, metro location, and broad skillset increase candidate competition.
Collections/BFSI preference increases domain specificity, limiting transferability across industries.
Strong mandatory ML, production and MLOps skills required but no fixed years specified.
Develop, validate, and deploy machine learning models focused on collections analytics to support predictive analytics solutions.
Build scalable, production-ready machine learning pipelines and monitor model performance continuously.
Collaborate with business stakeholders and technical teams to translate requirements into AI/ML solutions and optimize models based on business feedback.
Strong hands-on experience in machine learning, predictive analytics, and statistical modeling using Python and ML frameworks such as Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, or PyTorch.
Proficiency in feature engineering, model evaluation, hyperparameter tuning, model explainability, and supervised learning techniques.
Experience working with large structured datasets using SQL; knowledge of model deployment, monitoring, and MLOps.
Work Experience Required: Relevant years of overall experience in Data Science, Advanced Analytics, or Machine Learning with proven end-to-end solution deployment in production environments.
Expertise in collections analytics or BFSI domain, especially in areas like delinquency management, customer repayment behavior, and contact strategy optimization.
Demonstrated ability to execute end-to-end machine learning projects, including model development and production deployment in collaboration with cross-functional teams.
Familiarity with advanced ML workflows and tools, and optionally with cloud platforms (AWS, Azure, GCP) or foundational knowledge of GenAI/LLMs.