Sr. Data Scientist

Visa U.S.A. Inc., a Visa Inc. company, needs a Sr. Data Scientist (multiple openings) in Washington DC to

• Work within a team to generate business insights based on big data, identify actionable recommendations and communicate the findings to clients.
• Brainstorm innovative ways to use our unique data to answer business problems.
• Communicate with clients to understand the challenges they face and convince them with data.
• Extract and understand data to form an opinion on how to best help our clients and derive relevant insights.
• Develop visualizations to make your complex analyses accessible to a broad audience.
• Find opportunities to craft products out of analyses that are suitable for multiple clients.
• Work with stakeholders throughout the organization to identify opportunities for leveraging Visa data to drive business solutions.
• Mine and analyze data from company databases to drive optimization and improvement of product, marketing techniques and business strategies for Visa and its clients.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, data insights, and other business outcomes.
• Position reports to the Employer’s Washington, DC office and may allow for partial telecommuting.

Requirements

Basic Qualifications:

• Master's degree in Statistics, Operations Research, Applied Mathematics, or a related technical field and 2 years of experience in the job offered or in a Sr. Data Scientist-related occupation.
• Extracting and aggregating data from large data sets using SQL/Hive or Spark;
• Analyzing large data sets using programming languages like Python, R, SQL and/or Spark;
• Generating and visualizing data-based insights in software such as Tableau/R/Matplotlib;
• Building predictive and descriptive statistical models using machine learning tool kit, Jupyter notebooks, Python, and/or SAS; and
• Data mining and statistical modeling (e.g., regression modeling, clustering techniques, XGBoost, machine learning, decision trees, etc.)