Data Science Manager

Job Description

• Identify innovation opportunities around data and data-related processes that will help our clients implement fact-based decisioning processes within their cards and payments program.
• 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.

This is a hybrid position
. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

• Minimum of 8+ years of expertise in applying Machine Learning solutions to business problems – model development and production experience required.
• Post‑graduate degree (Masters or PhD) in a quantitative field such as Statistics, Mathematics, Data Science, Operational Research, Computer Science, Informatics, Economics, or Engineering.
• Experience working in one or more of the Card & Payments markets around the globe with specific responsibilities in payments, retail banking, or retail merchant industries. Airline industry experience is preferred.
• Good understanding of Payments and the Banking industry, including card verticals such as consumer credit, consumer debit, prepaid, small business, commercial and co‑branded product.
• Expert knowledge of data, market intelligence, business intelligence, and AI‑driven tools and technologies
  , with demonstrated ability to incorporate new techniques to solve business problems.
• Experience planning, organizing, and managing multiple large projects with diverse cross‑functional teams, including resource planning and delivery implementation.
• Experience in presenting ideas and analysis to stakeholders whilst tailoring data‑driven results to various audience levels.
• Proven ability to deliver results within committed scope, timeline, and budget.
• Ability to travel within MENA on short notice.

• Expertise in distributed computing environments /
  big data platforms (Hadoop, Elasticsearch) as well as common database systems (SQL, Hive, HBase).
• 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
  .
• Experience in drafting solution architecture frameworks that rely on API’s and micro‑services
  .
• 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
  .
• 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).