Lead Data Scientist

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.

Job Description

The Lead Data Scientist, you will play a crucial role in building our data capabilities, collecting, analyzing, driving insights, and delivering machine learning and AI solutions that enhance our offerings. Your will play a pivotal role in building the organization's "data muscle", empowering teams to leverage data for smarter decisions, operational excellence and innovation. This is a unique opportunity to work in a fast-paced and startup environment where your contributions will have a direct impact.

This role represents an exciting opportunity to make key contributions to Visa's strategic vision as a world-leading data-driven company. The role requires a seasoned professional with deep expertise in data strategy, analytics, governance paired with a hands-on and scrappy mindset to deliver impactful results. The successful candidate must have strong academic track record and demonstrate excellent software engineering skills. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.

To be successful in this position, you must be highly effective working both independently and in cross-functional capacities.

Essential Functions

• Formulate business problems as technical data problems while ensuring key business drivers are collected in collaboration product stakeholders.
• Work with product and engineering to ensure effective solutions. Deliver prototypes and production code based on need.
• Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
• Build needed data transformations on structured and un-structured data.
• Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, analytics, and statistical techniques.
• Devise and implement methods for efficiently monitoring model efficiency and performance in production.
• Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
• Contribute to development and adoption of shared predictive analytics infrastructure.
• Data mining, processing and analyzing large datasets to generate insightful reports.
• Develop data modeling processes and algorithms to derive meaningful actionable insights from structured and unstructured big data
• Utilize statistical analysis, data visualization, and machine learning techniques to extract meaningful insights from datasets. Identify and interpret patterns, trends, and correlations within