Sr.Analyst-Data Science

Compromise Decision Science Team of American Express is looking for an Senior Analyst who would be actively involved in developing, refining and implementing ML models for Compromise Fraud Risk Decision Sciences domain.

Compromise Decision sciences team is involved in developing solutions for preventing fraud arising from compromised card credentials. This involves developing sophisticated ML models, feature engineering, graph based solutions to curb fraud losses arising from cards that were compromised across the customer journey.

• Develop and enhance advanced machine learning and AI-driven solutions to identify emerging compromise patterns, detect suspicious behaviors, and strengthen risk decisioning capabilities.
• Apply cutting-edge techniques, including Graph Neural Networks (GNNs), graph-based learning, Transformers, Deep Learning, and other advanced ML algorithms, to solve complex business problems.
• Analyze large-scale, complex, and interconnected datasets to uncover hidden relationships, identify emerging risk signals, and generate actionable business insights.
• Design, develop, and optimize end-to-end analytical and modeling pipelines, including data preprocessing, feature engineering, model training, validation, and performance monitoring.
• Leverage Python, PySpark, and cloud-based platforms such as Google Cloud to build scalable, efficient, and robust analytical solutions.
• Explore and experiment with emerging AI/ML methodologies to continuously improve model effectiveness, predictive accuracy, and operational efficiency.
• Collaborate with cross-functional stakeholders to translate business challenges into innovative, data-driven solutions that deliver measurable business impact.
• Communicate complex analytical findings, model performance, and strategic recommendations effectively to both technical and non-technical audiences.
• Demonstrate strong analytical judgment, intellectual curiosity, and a proactive approach to identifying opportunities and solving complex business challenges.
  • Conduct extensive case reviews to generate insights for improving the model performance and decision-making processes
  • Collaborate with relevant stakeholders to ensure alignment of analytical and modeling efforts with business objectives, effectively managing stakeholder expectations and communication throughout the project lifecycle
  • Bring in ideas by incorporating external perspectives through reading research papers and identify appropriate use cases to enhance model development and innovation.

• Graduate or postgraduate degree in Statistics, Mathematics, Economics, Engineering, Management, Computer Science, or a related quantitative discipline from a recognized institution.
• 2–3 years of relevant experience in data science, advanced analytics, machine learning, or predictive modeling.
• Strong programming skills in Python and PySpark, with hands-on experience in data manipulation, statistical analysis, and machine learning model development.
• Solid understanding of machine learning and deep learning techniques, with exposure to Graph Neural Networks, graph-based algorithms, Transformer architectures, or related advanced AI methodologies.
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow and cloud computing environments such as Google Cloud Platform.
• Experience working with large-scale datasets, feature engineering, data preprocessing, and model evaluation techniques.
• Strong problem-solving skills, analytical rigor, and the ability to translate complex business requirements into effective technical solutions.
• Excellent written and verbal communication skills, with the ability to present analytical insights and recommendations to diverse stakeholders.
• Demonstrated ability to exercise sound judgment, work collaboratively, and deliver high-quality analytical solutions in a dynamic business environment.
  
  
• Shift Timings : Mon - Wed 1:00 PM -9:30 PM ; Thurs 11:00 AM- 7:30 PM ; Friday - 8:30 AM -5 PM