GCC Data Science Manager Visa

Work entails heavy focus on transaction data modelling and analytics for cards and payments products Work with a broader team that consists of Business Managers, Consultants and Data Scientists from both Visa and client organizations to strategies, co-create, deploy, and reap the benefits of data-driven solutions.

Work with regional and global Data Science teams to develop high-quality analytic products and solutions that promote Visa s growth in the region.

Keep Visa at the forefront of technological advancement in Data Science by introducing cutting-edge tools and techniques for generating business insights

Develop next-generation analytic methods where existing tools and techniques are inadequate to address business challenges

Review, direct, guide, and inspire the analytical work of junior members in the team

Collaborate with internal Technology partners and Data Engineering function to best leverage Visa s internal technology platforms, data, and the broader Visa ecosystem to support our clients technical data needs

Manage workload for self and any direct reports, providing prioritization guidance for project flow to improve process efficiency.

Manage and grow talent within the team.

Develop, share, and build global best practices and knowledge management within the team

Socialize innovative ideas and approaches that are scalable and have market demand

Champion internal requirements around Model Risk Management, Visa Analytics Rules, and Global Privacy standards around client delivery to ensure that Visa s highly regarded market standing is maintained

Technical Expertise

Expertise in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.).

Familiarity with both common computing environments (e.g., Linux, Shell Scripting) and commonly used IDE s (Jupyter Notebooks) proficiency in SAS technologies and techniques

Strong programming ability in different programming languages such as Python, R, Scala, Java, Matlab, C++, and SQL. Working knowledge of GENAI and Agentic AI tools and algorithms

Proficient in some or all of the following techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Markov Chain Monte Carlo, Gibbs Sampling, Evolutionary Algorithms (e.g. Genetic Algorithms, Genetic Programming), Support Vector Machines, Neural Networks, etc.

Expert knowledge of advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial, and multinomial regression, ANOVA) Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis) Decision Tree techniques (e.g., CART, CHAID)