This is a hybrid position.
Expectation of days in the office will be confirmed by your Hiring Manager.
Basic Qualifications:
Minimum of 6-8 years of analytics expertise in applying statistical solutions to business problems
Preferred Qualifications:
Experience working in one or more of the Card Payments markets around the globe
Post-graduate degree (Masters or PhD) in a Quantitative field such as Statistics, Mathematics, Operational Research, Computer Science, Economics, Engineering, or equivalent
Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded and merchant
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 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.
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)
Deliver results within committed scope, timeline and budget
Very strong people/project management skills and experience.
 
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.