Sr.Analyst-Data Science

American Express; Bengaluru; Hybrid; INR 1,500,000 - 2,300,000; Full time

Benefits offered by this job

Competitive salary

Bonus incentives

Flexible working model

Healthcare benefits

Job summary

American Express is seeking a Senior Analyst in Bengaluru to develop, refine, and implement ML models for compromise fraud risk decisioning. The role focuses on graph-based learning, deep learning, and scalable pipelines using Python, PySpark, and Google Cloud.

You will collaborate with cross‑functional teams, translate business challenges into data‑driven solutions, and communicate complex results to diverse audiences. 2–3 years in data science is expected.

Qualifications

• Graduate/postgraduate degree in a quantitative discipline.
• 2–3 years of data science/ML experience.
• Strong Python and PySpark programming skills.
• Exposure to Graph Neural Networks, graph‑based learning, Transformers or related AI methods.
• Experience with PyTorch or TensorFlow and Google Cloud Platform.

Responsibilities

• Develop and refine ML/AI solutions to identify compromise patterns and fraud risk.
• Apply Graph Neural Networks, graph-based learning, Transformers and other advanced ML techniques.
• Analyze large interconnected datasets to uncover risk signals and insights.
• Design end‑to‑end analytics pipelines: preprocessing, feature engineering, modeling, validation, monitoring.
• Collaborate with stakeholders to translate business problems into technical solutions and communicate results.

Skills

Python

PySpark

Data manipulation

Machine learning

Deep learning

Graph Neural Networks

Google Cloud

Communication skills

Education

Bachelor’s or Master’s in Statistics/Mathematics/Economics/Engineering/CS

Tools

PyTorch

TensorFlow

Google Cloud Platform

Job description

Job Description

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.

Job Description

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.

Responsibilities

• 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.

Qualifications

• 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

About Us

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world‑class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well‑being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

About The Team

We back you with benefits that support your holistic well‑being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:

• Competitive base salaries
• Bonus incentives
• Support for financial‑well‑being and retirement
• Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location)
• Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
• Generous paid parental leave policies (depending on your location)
• Free access to global on‑site wellness centers staffed with nurses and doctors (depending on location)
• Free and confidential counseling support through our Healthy Minds program
• Career development and training opportunities

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.

Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.