The Global Commercial Analytics (GCA) team a part of Global Strategy & Operations, GCS is the analytical engine that enables the Global Commercial Card business. The team drives Profitable Growth in Acquisitions through Data, Analytics, AI powered Targeting & Personalization Capabilities.
Purpose of the Role
This B35 role would be a part of GCA Global Prospecting Capabilities team, based out of Gurgaon, India, and will be responsible for AI/ML matching framework, tech platform expertise to build the commercial prospect database & optimize usage. You will be challenged with crafting and crafting world class prospect marketing analytics by leveraging machine learning and sophisticated methodologies.
Responsibilities
Evaluate, maintain, and continuously improve existing AI/ML matching algorithms based on the evolving needs and data availability, while supporting development and delivery of a vision, strategy, and roadmap to achieve a next-generation matching capability.
Explore the usage and implementation of data mining techniques, including regression analysis, clustering, decision trees, neural networks, and NLP solutions.
Experience in deploying and handling data products and solutions across a variety of platforms and technologies such as Big Data, PySpark, Hive, Scala, Python.
Bring a strong technical foundation; strong understanding coding practices, architecture, design, get under the hood of sophisticated coordinated architectures, coding systems, and interface design.
Collaborate optimally with product owners, designers, and a broad set of internal technical partners (across multiple internal groups).
Critical Factors to Success
Strong Programming skills are preferred. Experience with Big Data programming Languages (Hive, Spark), Python, Java is a must..
Strong analytical/conceptual thinking/statistical acumen to tackle unstructured and sophisticated business problems and articulate key findings to senior leaders/stakeholders in a succinct and concise manner
Basic knowledge of statistical techniques like hypothesis testing, regression, knn, ttest, chi-square test
Ability to work in a dynamic environment, with a strong attention to detail.
Academic Background
Graduate degree in a quantitative field (Finance, Engineering, Mathematics, Computer Science or Economics) is required.
Behavioral areas
High attention to detail with an eye for Accuracy & Controls, Strong results-orientation, resourcefulness, and flexibility to overcome significant obstacles to goal achievement in a fast paced environment.
Excellent communication skills (written and spoken) with ability to challenge the status quo and identify improvements.