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Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.
Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.
Qualifications:
Minimum of 5+ 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
Proven track record in commercializing analytical solutions.
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
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).
Strong programming ability in different programming languages such as Python, R, 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)
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
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
Fluency in English is mandatory
Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
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