Analyst - Data Science

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

At American Express, we know that with the right backing, people and businesses have the power to progress in incredible ways. Whether we’re supporting our customers’ financial confidence to move ahead, taking commerce to new heights, or encouraging people to explore the world, our colleagues are constantly redefining what’s possible — and we’re proud to back each other every step of the way. When you join #TeamAmex, you become part of a diverse community of over 60,000 colleagues, all with a common goal to deliver an exceptional customer experience every day. We back our colleagues with the support they need to thrive, professionally and personally. That’s why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.

As part of Credit and Fraud Risk capabilities Team, we help drive profitable business growth by reducing the risk of fraud and maintaining our customers' confidence in the security of our products. It utilizes an array of tools and ever-evolving technology to detect and combat fraud, minimize the disruption of good spending and provide a world-class customer experience. This exciting position is within the Credit & Fraud Risk Capabilities, Gurgaon, as the member responsible for building and maintaining the in-house applications that support best-in-class model implementation journeys for Credit and Fraud Risk models – globally to mitigate risks to the AXP brand, customer experience and Bank Holding Company status.

Function Description

Role Name: Analyst, Machine Learning and Risk Innovation

Credit and Fraud Risk (CFR) Organization with American Express is responsible for developing and monitoring Machine Learning models for predicting Consumer and Commercial behaviours such as credit risk, fraud risk, spending and revolve. These models are used for key business decisions made across the customer life cycle to manage risk and accelerate profitable business growth. Underpinning our growth as a company are the tools and capabilities that ensure we prudently take and manage risk in a viable way.

The Machine Learning and Risk Innovation team within CFR is responsible for pursuing new ML research that supports Amex risk decisions, including research and optimization of decision architectures, system design and optimization, and partnering with product strategy to enable new features and solutions.

Purpose of the Role:
Develop and enhance existing American Express statistical models by leveraging best-in-class modeling techniques and data across various stages of card member lifecycle.

Responsibilities

1. Drive innovation focused on enhancing the core Size of Wallet capability
2. Expand the scope of Size of Wallet capability to include suite of intelligence focused on understanding customer behaviour which would inform Amex’s strategies
3. Conduct research to build a framework for interpretability assessment of ML models
4. Design high-impact ML solutions for various risk use cases and to create efficiencies in the existing processes

Critical Factors to Success

Business Outcomes:

• Drive billing, revenue growth and profitability through advanced analytical techniques
• Ensure modeling accuracy and enhance modeling efficiency in existing processes using Machine Learning
• Innovate modeling techniques and variable creation
• Should possess strong analytical, problem-solving skills with research-oriented approach and a tremendous will to win.
• Sound knowledge of Machine learning algorithms/econometrics/statistics/data mining and research methods. Candidates with relevant certified patents will have an added advantage.

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

Academic Background

PhD or Post Graduate in Statistics/Mathematics/Economics/ Engineering/Management

Functional Skills

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, attribute engineering
• Deep learning, Gradient boosting machines, self-reinforcing algorithms

Technical Skills

Sound knowledge of Machine Learning algorithms and latest developments in Open-Source technologies.

Experience with capturing, managing and processing Big Data will be an added advantage.

Knowledge of Platforms

Hands on experience in open-source tools and techniques such as hive/python/pyspark