Data Science Manager

3 meses atrás

São Paulo, Brasil VISA Tempo inteiro

Job Description And Responsibilities

Develop and grow exceptional internal client relationships Develop and deploy analytical models and techniques within the organization and VCA’s clients
including issuers, acquirers and merchants Work with large volumes of data extract and manipulate large datasets using standard tools such as Hadoop Ecosystem, PySpark, SAS, Presto, SQL, etc. Hands-on skills in cleaning, manipulating, analyzing, visualizing large data sets. Data Cleansing/Wrangling – This involve parsing and aggregating messy, incomplete, and unstructured data sources to produce data sets that can be used in analytics/predictive modeling. Develop and validate advance data mining tools, algorithms, and other capabilities to solve business problem. Utilize Visa's data and analytic capabilities, technology, and industry expertise to develop, standardized and implement the consulting analytical solutions. Identify relevant market trends based on a deep analysis of payment industry Information. Interacting with several internal and external stakeholders for the strategic definition of analysis and initiatives. Continuously develop and present innovative ideas in order to improve current business practices within Visa. Perform client-specific analysis on portfolio data including proprietary information, such as customer demographics, activity, spend levels, fraud, credit risk and financial information. Communicate complex concepts and the results of the analyses in a clear and effective
manner. Support transfer technical knowledge to facilitate implementation of the business
solution provided. Document all projects developed and write other documentation as needed. Identify and
share best practices for key topics.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Qualifications

Basic Qualifications

5 or more years of relevant work experience with a Bachelor's Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD


Preferred Qualifications

6 or more years of work experience with a Bachelor's Degree or 4 or more years of relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up to 3 years of relevant experience with a PhD Strong interest in the future of payments a must. Experience in retail banking, payments, financial services, and/or technology industries Prior proven experience in Credit Risk or Fraud Prevention to manage and develop policies, ad-hoc analysis or models. Proven Hands-on experience in advanced analytics and statistical modeling including Linear Regression, Logistic Regression, Clustering methods (e.g. K-means), Classification methods (Decision Trees/Random Forest), among others. Experience working with large volumes of data (Big Data) - extracting, cleaning and manipulating large datasets using standard tools such as Python/PySpark, SQL, Hadoop/Hive, Presto, SAS, etc. Knowledge of visualization tools such as Tableau or PowerBI. Continuously develop and present innovative ideas based on data driven approach in order to improve current business practices within Visa, more specifically within the Risk Practice. Excellent project management, organizational and presentational skills. Strong teamwork, relationship management and interpersonal skills. Ability to multi-task various projects while meeting required deadlines. Fluency in English (spoken/written), Spanish is a plus

Additional Information

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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