Create and deliver powerful insights from data through better visualization and storyboarding Work with a broader team that consists of Business Managers, Consultants and Data Scientists from both Visa and client organizations to strategize, co-create, deploy and reap the
Work entails heavy focus on developing and implementing best-in-class risk analytic solutions, inclusive of scoring and non-scoring models.
Requirements
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 Ability to quickly understand and process alternate/non-conventional data sources/platforms and develop AI based advanced prediction algorithms Develop next-generation analytic
Experience: Minimum of 8+ years for Sr. Manager of expertise in applying Machine Learning solutions to business problems – model development and production
experience required Post-graduate degree (Masters or PhD) in quantitative fields such as Statistics, Mathematics, Data Science, Operational Research, Computer Science, Informatics, Economics, or Engineering Excellent knowledge,
experience and understanding of quantitative techniques (modelling, statistics, root-cause, etc.) applied to Risk Management with a focus on Card and Payments.
Familiarity with key Risk and Performance Indicators.
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 in presenting ideas and analysis to stakeholders whilst tailoring data-driven results to various audience levels Develop Risk advisory related knowledge and capabilities creating next generation risk engagement with Visa clients Partner with in-market VCA consultants and data scientists to deliver risk-related advisory engagements Establish Intellectual Property (IP) risk repository for VCA by capitalizing on learnings from consulting engagements (e.g., building standard methods, create library
experience Ability to travel within CEMEA 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.) Strong understanding and
experience of modern technology stack and microservice architecture including Kotlin, Spring boot, PostgreSQL, Kafka, AWS Relevant
experience of engineering unstructured/structured data from Telco, Supermarket, social media, Online logs, E-commerce, etc is preferrable Ability to write scratch MapReduce jobs and fluency with Spark frameworks Familiarity with both common computing environments (e.g.
Linux, Shell Scripting) and commonly used IDE’s (Jupyter Notebooks), proficiency in SAS technologies and techniques is preferred 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 Familiarity with common data modeling approaches and ability to work with various datatypes including JSON, XML, etc. Ability to build data pipelines (e.g.
ETL, data preparation, data aggregation and analysis) using tools such as NiFi, Sqoop, Ab Initio, familiarity with data lineage processes and schema management tools such as Avro 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.
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) 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
Benefits
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world.
As a Data Science Risk Manager, you will participate in building predictive and prescriptive models and develop context-based prototypes and high-impact storyboards that promote data-driven strategies and solutions for Visa clients.
Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
Additional details
Job Description The Sr Manager of Data Science is one of the risk roles in the Central Europe, Middle East and Africa (CEMEA) Data Science risk team.
We are seeking an innovative and analytical thinker to champion our data-driven strategies within the region.
The position will primarily focus on engagements in the area of credit, fraud and operational risks, deep risk analytics, risk scoring as well as forecasting solutions. Principal
Experience planning, organizing, and managing multiple large projects with diverse cross-functional teams, including resource planning and delivery implementation
Genetic Algorithms, Genetic Programming), Support Vector Machines, Neural Networks, Bagging and Boosting algorithms, etc.