Analyst-Data Science

RiseWithVishwas; Hinoba-an; Hybrid; PHP 1,592,000 - 2,123,000; Full time

Job summary

American Express in India is seeking an Analyst in Data Science within the Credit and Fraud Risk Center of Excellence. The role is hybrid in Gurugram or Bengaluru and focuses on building, deploying, and validating machine learning models for underwriting, risk, and fraud management.

The candidate will work with big data, predictive modeling, and data-driven insights to support global markets and senior leadership across functions.

Qualifications

• 2-4 years of experience in data science, risk analytics, or related field.
• MBA or Master’s in quantitative field required.
• Proficiency in Python/SAS/R/SQL and big data tools is preferred.

Responsibilities

• Develop, deploy, and validate predictive ML models for risk management, fraud detection, and customer acquisition.
• Analyze large structured and unstructured datasets to derive actionable insights.
• Engineer predictive attributes to enhance model accuracy across channels.
• Translate algorithmic outcomes into business narratives for stakeholders.
• Collaborate with global analytics, engineering, and business partners to productionize models.

Education

MBA / Master’s in Quantitative Field

Tools

Python

SAS

R

SQL

PySpark

Hive

MapReduce

HPC

Job description

2-4 Yrs Exp | MBA / Master’s in Quantitative Field | Data Science & Risk Analytics | Banking & Financial Services | Gurugram / Bengaluru, India | Hybrid | Full Time

Build, Deploy, and Validate Machine Learning Models to Drive Profitable Underwriting, Credit Risk, and Fraud Management at American Express

Join American Express as an Analyst in Data Science within the Credit and Fraud Risk (CFR) Center of Excellence. Operating on a hybrid work schedule in Gurugram or Bengaluru, you will leverage big data, advanced machine learning, and predictive modeling to optimize credit decisioning and combat financial fraud. You will analyze large-scale, closed-loop transaction data, develop supervised and unsupervised ML models, and communicate data-driven insights to senior business partners across global markets.

Key Responsibilities:

• Develop, deploy, and validate predictive machine learning models for risk management, fraud detection, and customer acquisition.
• Analyze massive unstructured and structured datasets to derive actionable business insights and optimize economic logic.
• Leverage closed-loop network data to engineer predictive attributes and enhance model accuracy across customer touchpoints.
• Translate complex algorithmic outcomes into structured business narratives for key stakeholders and executive leadership.
• Maintain cross-functional alignment with global analytics, engineering, and business partners to integrate models into production.

Tools/Technologies Used:

• Languages: Python, SAS, R, SQL
• Big Data & Distributed Computing: PySpark, Apache Hive, MapReduce, High-Performance Computing (HPC)
• Machine Learning Algorithms: Neural Networks, Decision Trees, Reinforcement Learning, Active Learning, Transfer Learning, Graphical Models, Gaussian Processes
• Analytics Techniques: Supervised & Unsupervised Learning, Attribute Engineering, Model Validation, Exploratory Data Analysis (EDA)