Data Scientist

seeking someone to build and deploy ML models (predictive, classification, clustering, forecasting) for business and financial use cases. Key responsibilities include EDA, statistical modeling for financial planning/risk, and translating business needs into analytical solutions. Technical stack: PySpark/Spark for large-scale data processing, MLflow for end-to-end ML lifecycle, and Feature Store frameworks for reusable pipelines. Experience in Payments, Cards, Banking, or Financial Services is a plus. Data Science & Machine Learning Role Overview: • Design, develop, and deploy machine learning models for business and financial use cases. • Build predictive, classification, clustering, recommendation, and forecasting solutions. • Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities. • Develop statistical models to support financial planning, forecasting, risk assessment, and performance optimization. • Translate business requirements into analytical solutions and measurable outcomes. • Process and analyze large-scale structured and semi-structured datasets using PySpark/Spark. • Develop efficient feature engineering pipelines for machine learning applications. • Work with distributed computing frameworks to support scalable model training and inference. • Implement end-to-end ML lifecycle management using MLflow. • Build and maintain reusable feature pipelines leveraging Feature Store frameworks. • Experience in the Payments, Cards, Banking, or Financial Services domain will be an added advantage. Data Science & Machine Learning Role Overview: • Design, develop, and deploy machine learning models for business and financial use cases. • Build predictive, classification, clustering, recommendation, and forecasting solutions. • Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities. • Develop statistical models to support financial planning, forecasting, risk assessment, and performance optimization. • Translate business requirements into analytical solutions and measurable outcomes. • Process and analyze large-scale structured and semi-structured datasets using PySpark/Spark. • Develop efficient feature engineering pipelines for machine learning applications. • Work with distributed computing frameworks to support scalable model training and inference. • Implement end-to-end ML lifecycle management using MLflow. • Build and maintain reusable feature pipelines leveraging Feature Store frameworks. • Experience in the Payments, Cards, Banking, or Financial Services domain will be an added advantage.