Data Scientist
Skills required
About this role
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.
Responsibilities
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.
Qualifications
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.
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