American Express is Hiring Analyst - Data Science | Gurugram/Bengaluru | Freshers-2 Years Exp

Job Description

American Express's Credit and Fraud Risk (CFR) team opening is a genuinely strong catch for data science candidates specifically because of its domain: fraud and credit risk modeling at a company processing millions of financial transactions daily is a different beast from typical marketing-analytics or product-analytics data science roles most freshers apply to. If you're deciding between multiple data science openings, this one offers exposure to a 𝐝𝐨𝐦𝐚𝐢𝐧 (𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐫𝐢𝐬𝐤) that's harder to break into later without prior experience — making it a strategically valuable first role even if the pay isn't the highest on the market.

The role's real technical center is predictive model development for high-stakes decisions — not exploratory analysis or dashboarding, but building models that directly influence credit and fraud decisions affecting real customers at scale. 𝐓𝐡𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐫𝐚𝐧𝐠𝐞 (𝟎-𝟑𝟎 𝐦𝐨𝐧𝐭𝐡𝐬) is unusually wide for a single posting, which suggests Amex is casting a broad net across 𝐟𝐫𝐞𝐬𝐡 𝐩𝐨𝐬𝐭𝐠𝐫𝐚𝐝𝐮𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐜𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞𝐬 𝐰𝐢𝐭𝐡 𝐮𝐩 𝐭𝐨 𝟐.𝟓 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 — meaning fresh MBA/Master's graduates are competing in the same pool as candidates with meaningful prior analytics work, so a strong academic project portfolio matters more here than at roles targeting freshers exclusively.

𝐒𝐤𝐢𝐥𝐥𝐬-𝐰𝐢𝐬𝐞, the listing's technique list (active learning, transfer learning, graphical models, Gaussian processes, Bayesian models) goes well beyond standard "logistic regression and decision trees" fresher-level data science asks — this signals Amex wants candidates who've gone deeper than a typical online ML course, likely through a Master's thesis, research project, or genuinely rigorous coursework. For most candidates, the realistic gap to close before applying isn't the programming languages (SAS/R/Python/SQL are commonly taught) but genuine exposure to these more advanced statistical/ML techniques specifically.

𝐎𝐮𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 :

- 𝐆𝐨𝐨𝐝 𝐟𝐨𝐫: MBA or Master's graduates in Statistics/Economics/CS with genuine exposure to advanced ML techniques (not just intro-level coursework) who want financial-domain data science experience

- 𝐍𝐨𝐭 𝐢𝐝𝐞𝐚𝐥 𝐟𝐨𝐫: those seeking a pure software engineering or MLE role — this is analytics-and-modeling focused, closer to applied statistics than production ML engineering

- 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲: 𝐇𝐢𝐠𝐡 — the combination of a Master's-level qualification requirement, broad technique list, and competition from candidates with up to 30 months of experience makes this more competitive than a typical fresher data role

- 𝐂𝐚𝐫𝐞𝐞𝐫 𝐭𝐫𝐚𝐣𝐞𝐜𝐭𝐨𝐫𝐲: commonly leads into 𝐒𝐞𝐧𝐢𝐨𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐭, 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭, or 𝐑𝐢𝐬𝐤 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐋𝐞𝐚𝐝 roles within Amex's analytics organization, with financial-domain experience being a strong differentiator for future fintech/banking data science roles

𝐂𝐨𝐦𝐩𝐞𝐧𝐬𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: Amex hasn't disclosed pay for this role. Analyst-level data science roles at large financial services companies in India (Gurugram/Bengaluru) with a Master's/MBA requirement typically range around ₹8–14 LPA based on general market data for comparable analytics roles at multinational financial firms — this is a broad estimate given the wide experience range accepted, and actual offers will vary significantly based on candidate background.

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Given the advanced technique list in the requirements, use your resume/cover letter to specifically name 1-2 techniques from their list that you've actually applied in a project (e.g., "built a fraud detection model using Gaussian mixture models on transaction data") rather than a generic "proficient in machine learning" claim — Amex's CFR team is explicitly asking for depth in specific methods, and matching their exact vocabulary with a concrete example is likely to stand out in an application pool this competitive.

Roles & Responsibilities

The role centers on a full-cycle involvement in model-driven decision-making — from understanding the underlying business logic through to communicating findings to leadership — rather than being purely a backend modeling function isolated from business context.

- Understand core business drivers behind Amex's risk, fraud, and marketing decisions

- Analyze large-scale data to derive insights and build innovative solutions

- Leverage Amex's network data ("closed loop") to make decisions more intelligent

- Develop newer approaches using big data and machine learning

- Clearly communicate business findings to leadership and cross-functional partners

- Stay current on developments in finance, payments, and analytics fields

Qualifications & Eligibility

Unlike many fresher-level analytics roles, this one requires a postgraduate qualification as a baseline, not just any bachelor's degree — a meaningful filter worth being aware of before applying.

- MBA or Master's in Economics, Statistics, Computer Science, or related fields

- 0–30 months of experience in analytics or big data workstreams

- Ability to work independently on complex, unstructured problems

- Strong communication skills for cross-functional, global collaboration

Skills Required

The core programming/query languages are standard for data science roles, but the ML technique list goes noticeably deeper than typical entry-level postings — worth reviewing carefully against your own project experience before applying.

- SAS, R, Python, Hive, Spark, SQL

- Supervised/unsupervised ML techniques: decision trees, neural models, Bayesian models, graphical models, reinforcement learning

- Advanced techniques: active learning, transfer learning, Gaussian processes, Map Reduce, attribute engineering

- Preferred: strong coding, algorithms, high-performance computing expertise