Data Science Manager

Identify innovation opportunities around data and datarelated processes that will help our clients implement factbased 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 cocreate deploy and reap the benefits of datadriven solutions Work with regional and global Data Science teams to develop highquality 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 cuttingedge tools and techniques for generating business insights Develop nextgeneration 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 23 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 Qualifications Minimum of 8 years of expertise in applying Machine Learning solutions to business problems model development and production experience required Postgraduate 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 cobranded product Expert knowledge of data market intelligence business intelligence and AIdriven tools and technologies with demonstrated ability to incorporate new techniques to solve business problems Experience planning organizing and managing multiple large projects with diverse crossfunctional teams including resource planning and delivery implementation Experience in presenting ideas and analysis to stakeholders whilst tailoring datadriven results to various audience levels Proven ability to deliver results within committed scope timeline and budget Ability to travel within MENA on short notice 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 Experience in drafting solution architecture frameworks that rely on API s and microservices Proficient in some or all of the following techniques Linear Logistic Regression Decision Trees Random Forests KNearest 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 You will be redirected to the company website to apply for this position