Analyst-Data Science

AMERICAN EXPRESS; Sultanpur; On-site; INR 800,000 - 1,200,000; Full time

Job summary

American Express in India seeks a results-driven analytics professional to join the Model Risk Management Group (MRMG). The role involves independent oversight of AI/ML-based models across marketing, credit, fraud, and other risk areas, with cross-functional collaboration to drive validation initiatives.

The candidate should have 0-2 years in analytics, strong technical skills (Python, R, SQL, SAS), and robust communication abilities to convey complex results to business partners and senior

Qualifications

• 0-2 years experience in analytics or big data workstreams.
• MBA or Master’s Degree in Economics, Statistics, or related fields from a top-tier institute.
• Hands-on model development or validation experience.
• Strong analytical and relationship and project management skills for driving validation initiatives.
• Experience applying advanced statistical/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; ability to adapt to tight deadlines and changing priorities.
• Experience with data manipulation tools such as Python, R, Java, SQL and SAS is a must have.
• Data Science/ML/AI, expertise in coding and various ML techniques including supervised/unsupervised methods.

Responsibilities

• Manage and control model risk for next-generation AI/ML-based models.
• Elevate model excellence and adapt to evolving regulatory expectations.
• Conduct independent oversight of enterprise-wide models focusing on AI/ML across marketing, credit, fraud, and other risk types.
• Conduct gap assessments and establish robust framework to strengthen model risk controls.
• Conduct research to elevate model excellence and drive business impact.
• Communicate results to partners, senior leadership and model committees.
• Lead with enterprise thinking and collaboration to maximize business returns and innovation.

Skills

Analytical skills

Project management

Communication

Teamwork

Education

MBA

Economics/Statistics MS

Tools

Python

R

SQL

SAS

Java

Job description

Job Summary

As part of Team Amex, youll 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. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. Model Risk Management Group (MRMG) within Global Risk and Compliance Group, is responsible for the independent risk management of all American Express (AXP) models.

Requirements

• 0-2 years experience in analytics or big data workstreams
• Academic Background: 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 Python, R, JAVA, SQL and SAS is a must have.
• Data Science/ Machine Learning/ Artificial Intelligence, Expertise in Coding, Supervised and Un supervised Techniques - active learning, transfer learning, neural models, decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian

Technical Skills & Capabilities

• Experience with at least one of the data manipulation tools such as Python, R, JAVA, 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

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 Courage

Responsibilities

• 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 marketing, 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
• Communicating results to partners, senior leadership and various model committees
• Critical Factors to Success:
  • 1.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
  • 2. 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