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.
ResponsibilitiesData 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.
QualificationsData 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.
About UsSkills Required
- 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 and identify trends, anomalies, and business opportunities
- Develop statistical models for 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 or Spark
- Develop feature engineering pipelines for machine learning applications
- Use distributed computing frameworks for scalable model training and inference
- Implement end-to-end machine learning lifecycle management using MLflow
- Build and maintain reusable feature pipelines using Feature Store frameworks
- Experience in Payments, Cards, Banking, or Financial Services
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
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