American Express - Analyst - Risk Management II - Machine Learning/Artificial Intelligence/Data Science (0-6 Yrs) Gurgaon/Gurugram (Systems/Product Software)

Job Description - Why American Express? Theres a difference between having a job and making a difference.American Express has been making a difference in peoples lives for over 160 years, backing them in moments big and small, granting access, tools, and resources to take on their biggest challenges and reap the greatest rewards. Weve also made a difference in the lives of our people, providing a culture of learning and collaboration, and helping them with what they need to succeed and thrive. We have their backs as they grow their skills, conquer new challenges, or even take time to spend with their family or community. And when theyre ready to take on a new career path, were right there with them, giving them the guidance and momentum into the best future they envision. Because we believe that the best way to back our customers is to back our people. The powerful backing of American Express. Dont make a difference without it. Dont live life without it. Function Description: - Global Risk Banking and Compliance work to enhance the efficiency and effectiveness of control and compliance at American Express in order to successfully identify, monitor and manage risks.
-Model Risk Management Group (MRMG) is an organization within Global Risk Banking and Compliance (GRBC) and engages in model risk management through various activities including independent validation of all models across American Express.
-Team is responsible for validation of a variety of mathematical and statistical models developed and used in Customer Management, Prospecting etc. Team validates models, identify gaps, articulate findings, write reports, and present summary to senior leaders and regulators. Purpose of the Role : - Mitigate risk elevate the quality of model through independent oversight to validate models and improve model risk management Responsibilities : - Independently validate consumer, small business and institutional machine learning models and algorithms which include evaluating conceptual soundness, performance validation and on-going monitoring.
-Exploring the applicability of machine learning techniques in AXP models.
-Validating and maintaining model/ algorithm inventory - Documenting detailed model/algorithm validation report and preparing for the regulatory and internal audit reviews.
-Tracking the validation findings and reviewing the resolution of findings.
-Communicating the validation results to partners, senior leadership and various model committees.
-Conducting research to enhance model risk areas or model validation process and techniques.
-Assist in the development of second-line monitoring capabilities Critical Factors to Success: Business Outcomes: - Effectively challenge the conceptual soundness, theory and approach, purpose/usages of predictive models/ financial frameworks - Effectively challenge the statistical techniques and model performance outcomes for appropriateness and integrity Articulate reasoning behind each finding with accuracy and comprehensiveness.
-Knowledge of regulatory guidance and best practices. 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 Qualifications : Past Experience : 0-6 years of experience in Strong analytical and technical skills Preferred: Ability to write professional grade technical reports and presentations Academic Background : Post Graduate Degree in Management/Decision Science Functional Skills/Capabilities : - Data Science/Machine Learning/Artificial Intelligence - Expertise in Coding, Algorithm, High Performance Computing - unsupervised and supervised techniques : active learning, transfer learning, neural models, Decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian models, map reduce techniques, Random Forrest, Gradient Boosting - Deep Learning - Text mining algorithms Technical Skills/Capabilities : Analytics Insights Targeting - R, Python, C, C , Java, SAS SQL - Advanced Statistical Techniques - Back testing, and sensitivity testing - knowledge of applied econometrics - Knowledge of Platforms : Hadoop - Big Data - Cornerstone 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 C...

Required Skills : English