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.