Sr.Analyst-Data Science
American Express (company site, opens in a new tab)2–3 yrsBengaluruOn-siteDirect career pageEst. 18–30 LPA
Posted 10h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at American Express
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at American Express you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, refine, and implement machine learning models for Compromise Fraud Risk Decision Sciences domain focused on preventing fraud from compromised card credentials.
- Perform feature engineering, large-scale data manipulation, and deploy graph-based solutions including Graph Neural Networks to reduce fraud losses.
- Translate complex business requirements into effective technical solutions that support fraud detection across the customer journey.
Minimum Requirements
- Graduate or postgraduate degree in Statistics, Mathematics, Economics, Engineering, Management, Computer Science, or related quantitative discipline.
- 2-3 years of experience in data science, advanced analytics, machine learning, or predictive modeling.
- Strong programming skills in Python and PySpark with experience in ML model development and data analysis.
- Experience with machine learning/deep learning techniques including Graph Neural Networks and frameworks like PyTorch or TensorFlow; familiarity with Google Cloud Platform.
Ideal Candidate Profile
- Experienced in developing and deploying sophisticated ML models to combat fraud, particularly with graph-based AI methods.
- Able to work on large-scale datasets and perform advanced feature engineering and data preprocessing independently.
- Comfortable translating complex, dynamic business fraud scenarios into analytical solutions within a risk-focused environment.