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Senior Manager, Data Science
Senior Manager, Data Science
The Senior Manager, Data Science will champion data-driven strategies for Visa clients in the MENA region, with a dedicated focus on a key client account in the UAE. This role involves building predictive models, developing high-impact storyboards, and leading a team in leveraging advanced analytics for payment and card products.
About the role
Job Description
For the Senior Manager of Data Science role in the Middle East and North Africa (MENA) Data Science team, we are seeking an innovative and analytical thinker to champion our data-driven strategies within the region. As a Data Science Senior Manager, you will participate in business development, build predictive and prescriptive models, and develop context-based prototypes and high-impact storyboards that promote data-driven strategies and solutions for Visa clients. This role is dedicated exclusively to working for a key client account based in the UAE market.
Principal Responsibilities
- 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 organisations to strategise, 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 prioritisation 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.
Qualifications
- Minimum of 10+ 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.
- 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.
- Very strong people/project management skills and experience.
- 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, Pyspark Scala, 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, 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).
- Hands-on experience on Azure Machine Learning platform starting from feature engineering, development, validation, and deployment of the models.
Leadership Competencies
- Demonstrates integrity, maturity, and a constructive approach to business challenges.
- Serves as a role model for the organization by implementing core Visa Values.
- Shows respect for individuals at all levels in the workplace.
- Strives for excellence and extraordinary results.
- Uses sound insights and judgments to make informed decisions in line with business strategy and needs.
- Able to allocate tasks and resources across multiple lines of businesses and geographies.
- Able to influence senior management both within and outside Data Science.
- Successfully persuading internal stakeholders to commit to best-in-class solutions, when required.
- Leverages change management leadership as required.