• Minimum of 7+ years of analytics expertise in applying statistical solutions to business problems.
• 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.
• Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded and merchant portfolios.
• Experience working in one or more of the Card and Payments markets around the globe.
• Familiarity in working with big data, both structured and unstructured.
• Proven ability to develop high-quality, production-ready quantitative models for business consumption; machine learning techniques preferred.
• Working knowledge of code optimization best practices for run-time performance.
• Post-graduate degree (Masters or PhD) in a Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, Engineering, or equivalent.
• Good knowledge of data, market intelligence, business intelligence, and AI-driven tools and technologies.
• Experience planning, organizing, and managing multiple large projects with diverse cross-functional teams.
• Demonstrated ability to incorporate new techniques to solve business problems.
• Demonstrated resource planning and delivery skills.
• Experience in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.).
• 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.
• Strong programming ability in different programming languages such as Python, R, Scala, Java, Matlab, C++, and SQL.
• Experience in solution architecture frameworks that rely on API s and micro-services.
• Familiarity with common data modeling approaches; ability to work with various datatypes including JSON, XML, etc.
• Familiarity with building data pipelines (e.g. ETL, data preparation, data aggregation and analysis) using tools such as NiFi, Sqoop, Ab Initio; practical experience 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, Gradient Boosting, 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).
• Experience with model governance processes in a highly regulated industry; financial services preferred.
• Deliver results within committed scope, timeline and budget.
• Very strong people/project management skills and experience.
Ability to travel within CEMEA on short notice.
• Results-oriented with strong problem solving skills and demonstrated intellectual and analytical rigor.
• Good business acumen with a track record in solving business problems through data-driven quantitative methodologies.
• Experience in Cards and Payments, Retail Banking, or Retail Merchant industries preferred.
• Very detailed oriented, is expected to ensure highest level of quality/rigor in reports and data analysis.
• Proven skills in translating analytics output to actionable recommendations and delivery.
• Experience in presenting ideas and analysis to stakeholders whilst tailoring data-driven results to various audience levels.
• Demonstrates integrity, maturity and a constructive approach to business challenges.
• Serves as a role model for the organization and implementing core Visa Values.
• Maintains 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 business and geographies.
• Demonstrates ability to influence senior management within and outside Data Science groups.
• Can successfully persuade/influence internal stakeholders towards building best-in-class solutions.
• Provides change management leadership.
• Team oriented, collaborative, diplomatic, and flexible style.
• Exhibits intellectual curiosity and a desire for continuous learning.