Data Scientist
EXL
Data Scientist
EXL
Analyzes business data and runs outsourced operations
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.
EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.
Explore more opportunities at EXL