Analyst-Data Science

Gurugram, Haryana, India | ID: 25016273 | Hybrid

Job Description

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. As part of Team Amex, you’ll experience this 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.

Join Team Amex and let’s lead the way together.

How will you make an impact in this role?

Model Risk Management Group (MRMG) within Global Risk and Compliance Group, is responsible for the independent risk management of all American Express (AXP) models.

Purpose of the Role: The successful candidate will be responsible to manage and controls model risk, specifically associated to the next generation Artificial Intelligence or Machine Learning based models. This role will elevate model excellence, strengthen long term shareholder value, and adapt to the changing landscape of both model development innovation, external environment and heightened regulatory expectations. The specific responsibilities include: –

• Conduct independent oversight of enterprise-wide models with a focus on Artificial Intelligence and Machine Learning based models for credit, fraud, or other business/ risk types.
• Conduct gap assessments and establish robust framework to strengthen model risk controls and meet heightened regulatory standards
• Conduct research to explore opportunities to elevate model excellence and drive business impact
• Seek and incorporate external perspective in day-to-day work and projects
• Communicating results to partners, senior leadership and various model committees

Critical Factors to Success:

• Business Outcomes: Effectively challenge the conceptual soundness, theory and approach, purpose/usages of predictive models
• Maximize business returns by institutionalizing efficient and accurate models.
• Innovate modeling techniques and variable creation
  Ensure modeling accuracy and enhance modeling efficiency in existing processes using Machine Learning              
• Leadership Outcomes: Put enterprise thinking first, connect the role’s agenda to enterprise priorities and balance the needs of customers, partners, colleagues & shareholders.
• Lead with an external perspective, challenge status quo and bring continuous innovation to our existing offerings
• Demonstrate learning agility, make decisions quickly and with the highest level of integrity
• Lead with a digital mindset and deliver the world’s best customer experiences every day

Past Experience: 0-2 years’ experience in credit and fraud analytics, machine learning or both – Academic Background: B.tech/B.Eng, MBA, Master’s Degree In Economics, Statistics Or Related Fields From Top Tier Institute

Functional Skills/Capabilities:

• Hands-on model development or validation experience.
• Strong Analytical and Relationship and project management skills for driving validation initiatives.
• Experience in applying advanced statistical and/or quantitative techniques to solve business problems is preferred.
• Good Verbal, Written, Interpersonal skills and ability to work effectively in a team environment.
• Willingness to Collaborate with Cross-Functional teams to drive validation and Project Execution.
• Effectively communicating complex Analytical results to Business Partners and Senior Management.
• Flexibility and Adaptability to Work Within tight deadlines and changing priorities. –

Technical Skills/Capabilities:-

• Experience with at-least one of the data manipulation tools such as R, Python, SQL and SAS is a must have.
• Data Science/ Machine Learning/ Artificial Intelligence, Expertise in Coding, Supervised and Unsupervised Techniques – active learning, transfer learning, neural models, decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian models, map reduce techniques, Random Forest, Gradient Boosting, Deep Learning, Text Mining Algorithms.

Behavioral Skills/Capabilities: Enterprise Leadership Behaviors:- Set The Agenda: Define What Winning Looks Like, Put Enterprise Thinking First, Lead with an External Perspective – Bring Others With You: Build the Best Team, Seek & Provide Coaching Feedback, Make Collaboration Essential – Do It The Right Way: Communicate Frequently, Candidly & Clearly, Make Decisions Quickly & Effectively, Live the Blue Box Values, Great Leadership Demands

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.

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Position Details

• Role Title: Analyst – Model Risk Management (AI/ML Focus)
• Company: American Express
• Location: Gurugram, Haryana, India
• Work Mode: Hybrid
• Experience Level: 0–2 years

Compensation

• Expected CTC: ₹12 – 18 LPA (entry-level analytics/ML role at Amex India)
• Additional Benefits: Performance bonus, healthcare, stock purchase plan, wellness allowance, learning & leadership programs.

Tech Stack & Tools

• Programming: Python, R, SQL, SAS
• ML/AI Techniques: Supervised & Unsupervised Learning, Neural Networks, Random Forest, Gradient Boosting, Deep Learning, Transfer Learning, Reinforcement Learning, Text Mining, Bayesian Models
• Other: Model validation frameworks, data manipulation libraries, regulatory risk standards

Day-to-Day Work You Can Expect

• Validating & providing oversight on AI/ML credit and fraud models.
• Conducting gap assessments and strengthening risk controls.
• Researching new ML techniques to improve model excellence.
• Collaborating with cross-functional teams to execute validation projects.
• Communicating findings with leadership and risk committees.

Skills That Will Help You Stand Out

• Strong statistics & ML fundamentals (bias/variance, cross-validation, ROC/AUC).
• Hands-on experience in Python (pandas, scikit-learn, TensorFlow/PyTorch basics).
• SQL for large dataset handling.
• Knowledge of credit/fraud risk modeling.
• Ability to explain complex models in simple business terms.

Career Growth & Outlook

• Early exposure to AI/ML in financial risk management gives you a unique edge.
• After 1–2 years, you can move into roles like Senior Risk Analyst, Data Scientist, or Quantitative Modeler.
• Long term: opportunities in Risk Strategy, Model Governance, FinTech AI, or Global Analytics leadership roles.

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