Apply for the Analyst – Data Science role at American Express in Gurugram/Manesar (1-3 years of experience). Drive customer acquisition via Paid Search using SQL, Python, ML, and GenAI.

About the Company: American Express

American Express (Amex) is a globally integrated payments company that provides customers with access to products, insights, and experiences that enrich lives and build business success. Renowned for its premier credit cards, travel services, and financial network infrastructure, Team Amex relies heavily on data-intensive technology to sustain its premium market position

This role is housed within the Analytics, Investments and Marketing Enablement (AIM) team, a core technical division of Global Commercial Services Marketing (GCSM). The AIM team is the analytical engine powering customer acquisition, user engagement, and cardholder retention across global B2B markets. Operating from Amex’s state-of-the-art campus in Gurugram, the division functions as an in-house center of excellence where data scientists build scalable digital frameworks backed by institutional computing infrastructure.

About the Role: Analyst – Data Science (Paid Search & Acquisition)

Are you a quantitative problem solver eager to combine classical machine learning with cutting-edge Generative AI to completely optimise digital search acquisitions? American Express is hiring a full-time, permanent Analyst – Data Science based at its corporate facility in Gurugram / Manesar, Haryana, India. This entry-to-mid-level role is tailored for data specialists with 1 to 3 years of experience who possess strong statistical foundations and a deep curiosity for unstructured business problems.

As a member of the Paid Search Analytics – GCS Digital Acquisitions squad, your primary mandate is to eliminate performance inefficiencies across paid acquisition channels. Instead of passive data monitoring, you will actively build analytical models that dictate automated bidding logic, decode user intent, run search auction experiments, and maximise marketing yield. This position offers a highly visible track to embed Large Language Models (LLMs) and advanced regression models into live, multinational digital acquisition environments.

Key Responsibilities & Data Science Workflows

• Acquisition Model Engineering: Build, fine-tune, and deploy predictive machine learning models (such as XGBoost, Decision Trees, K-Means clustering, and Regression architectures) to segment high-value commercial prospects and improve targeting accuracy

• GenAI Innovation & Search Intelligence: Apply advanced Natural Language Processing (NLP) and Generative AI / LLM frameworks to execute automated insights generation, semantic keyword analysis, customer intent modelling, and intelligence extraction across changing search ecosystems.

• Bidding & Auction Optimisation: Deconstruct complex Paid Search bidding algorithms, evaluate auction dynamics, and build automated mathematical frameworks to improve campaign visibility and lower cost-per-acquisition metrics.

• Rigorous Growth Experimentation: Scope, design, and evaluate robust A/B testing frameworks and performance measurement solutions to quantify the financial uplift of digital targeting tweaks.

• Large-Scale Data Cleansing & Synthesis: Mine, map, and process massive volumes of structured and unstructured web performance logs, consumer journeys, and transactional arrays.

• Cross-Functional Partner Alignment: Translate technical modelling frameworks and statistical outcomes into clear, plain-language operational strategies for marketing, product, and platform technology teams.

Candidate Prerequisites & Key Technical Skills

Successful candidates must balance mathematical logic and clean coding practices with the strong communication skills needed to influence cross-functional business partners.

Required Education & Professional Tenure:

• Experience Benchmark: 1 to 3 Years of professional track record within a dedicated Data Science, Business Intelligence, or Advanced Analytics domain.

• Academic Credentials: Master’s Degree in a highly quantitative discipline (e.g., Data Science, Engineering, Mathematics, Computer Science, Statistics, Finance, or Economics).

• Mindset Attribute: Exceptional conceptual thinking capacity with a proven knack for turning highly unstructured, ambiguous business dilemmas into clean data science solutions.

   

Core Technical Stack Competencies:

• Analytical Coding Mastery: Advanced programmatic proficiency in Python (including packages like pandas, scikit-learn, and numpy) or similar computing languages.

• Database Engineering Foundations: Complete fluency in SQL to query, structure, and manipulate relational tables across large enterprise data storehouses.

• Statistical Modeling Capabilities: Solid understanding of machine learning algorithms, including decision trees, boosting methods, clustering groupings, and regression variations.

   

• Emerging AI Familiarity: Hands-on exposure to or projects built with Large Language Models (LLMs), prompt parameters, or foundational Generative AI tools.

   

• Domain Strengths (Preferred Plus): Prior background in digital marketing analytics, personalization algorithms, search engine marketing (SEM) systems, or enterprise big data infrastructure (Spark/Hive) is a major advantage.

   

Core Position Specifications

• Position Title: Analyst – Data Science (SQL, Python, GenAI)

• Hiring Organization: American Express (TRS India)

• Job Location: Gurugram / Manesar, Haryana, India (Hybrid Operating Framework)

• Experience Allotment: 1 – 3 Years

• Compensation Profile: Competitive Base Salary (Not Disclosed / Best in Market Benchmarks) + Year-End Performance Bonuses

• Employment Framework: Full-Time, Permanent corporate appointment

• Industry Placement: Financial Services / Banking / Analytics Hub

• Organizational Department: Data Science & Analytics – Other

• Target Career Area: Analytics & Risk Management