Analyst – Data Science

The American Express interview process typically spans two to three rounds. The first round assesses quantitative aptitude and logical reasoning, followed by a technical round that tests data‑science fundamentals, coding ability, and case‑study analysis. The final HR round evaluates cultural fit, communication skills, and motivation. Candidates should focus on clear problem‑solving approaches, strong statistical knowledge, and articulating business impact of their solutions.

Round 1: Online Aptitude & Logical Reasoning → Round 2: Technical Data Science (coding, case study, ML concepts) → Round 3: HR interview focusing on fit and motivation

American Express promotes a high‑performance culture with a strong emphasis on diversity, inclusion, and work‑life balance. Employees enjoy mentorship programs, continuous learning opportunities, and clear growth pathways within a supportive environment.

Graduates (B.Tech/B.E., B.Sc., B.Com, MCA, M.Tech) in Computer Science, Information Technology, Electronics, Statistics, Mathematics, Economics or related fields; Minimum 60% aggregate; Batch 2025 or 2026; No active backlogs at the time of application; Freshers eligible.

American Express (Amex) is a globally recognized financial services company with a legacy of over 160 years in delivering innovative payment solutions, travel services, and merchant financing. In India, Amex has built a strong presence across major metros, partnering with thousands of merchants and serving millions of cardholders. The company prides itself on a customer‑centric culture, high ethical standards, and a commitment to technology‑driven transformation. Employees benefit from a collaborative environment, continuous learning programs, and exposure to cutting‑edge financial products that shape the future of payments. The Analyst – Data Science role is an entry‑level position designed for fresh graduates who are passionate about turning large, complex datasets into actionable business insights. As a Data Science Analyst, you will work closely with product managers, business analysts, and senior data scientists to understand the core business levers of American Express, develop predictive models, and support data‑driven decision making across the organization. This role offers a unique blend of analytical rigor, business acumen, and exposure to the fast‑moving payments ecosystem. Key responsibilities include: 1. Understanding Amex’s core business processes and identifying data‑driven opportunities to improve decision making. 2. Analyzing large volumes of transactional and behavioral data to extract insights and support strategic initiatives. 3. Building and validating machine‑learning models for fraud detection, customer segmentation, and spend forecasting. 4. Leveraging Amex’s closed‑loop network to create intelligent, real‑time recommendations for merchants and cardholders. 5. Communicating findings clearly through visual dashboards and presentations to senior leadership and cross‑functional partners. 6. Staying updated on emerging trends in finance, payments, and analytics to bring innovative solutions to the team. 7. Collaborating with engineering teams to ensure data pipelines are robust, scalable, and secure. 8. Documenting methodologies, experiment results, and best practices for knowledge sharing. 9. Participating in hackathons and internal innovation challenges to foster a culture of continuous improvement. 10. Assisting in the development of data‑governance frameworks and compliance standards. The typical tech stack includes Python (pandas, scikit‑learn, TensorFlow), SQL, R, Hadoop/Spark for big‑data processing, Tableau/PowerBI for visualization, and cloud platforms such as AWS or Azure. Career progression can move from Analyst to Senior Analyst, then to Data Scientist, Lead Data Scientist, and eventually to Manager or Director of Analytics. Joining American Express offers the chance to work with a world‑class brand, gain exposure to global best practices, and develop a deep understanding of the payments industry while building a solid foundation in data science.