Principal Data Scientist (Real Time Payment Modeling)

  • Full-time
  • Job Family Group: Product Development

Company Description

As the world's leader in digital payments technology, Visa's mission is to connect the world through the most creative, reliable and secure payment network - enabling individuals, businesses, and economies to thrive. Our advanced global processing network, VisaNet, provides secure and reliable payments around the world, and is capable of handling more than 65,000 transaction messages a second. The company's dedication to innovation drives the rapid growth of connected commerce on any device, and fuels the dream of a cashless future for everyone, everywhere. As the world moves from analog to digital, Visa is applying our brand, products, people, network and scale to reshape the future of commerce.

At Visa, your individuality fits right in. Working here gives you an opportunity to impact the world, invest in your career growth, and be part of an inclusive and diverse workplace. We are a global team of disruptors, trailblazers, innovators and risk-takers who are helping drive economic growth in even the most remote parts of the world, creatively moving the industry forward, and doing meaningful work that brings financial literacy and digital commerce to millions of unbanked and underserved consumers.

You're an Individual. We're the team for you. Together, let's transform the way the world pays.

Job Description

Visa has the world’s largest consumer payment transaction data set.  We see data on over 100 billion transactions per year from all over the world. We use that data to help our clients in the payment ecosystem grow their businesses and to help consumers access a fast, safe, and rewarding payment experience.

From across the globe, people are increasingly relying on digital payments and mobile technology to use their money any time, make purchases online, transfer funds across borders and access basic financial services. To ensure that payment transactions are secure and reliable, Visa has invested heavily in advanced authentication and fraud prevention technologies, and the company continues to develop and deploy new and innovative programs to fight fraud, enable acceptance, and support consumers. We also invest heavily in helping banks and merchants understand the behavior of their customers so they can bring products and services to market that more effectively meet their customers’ needs.

Reporting into the Sr. Director of Data Science, you will be key player on the Real Time Payment (RTP) Modeling Team, responsible for building and maintaining production-level Predictive Models to support the new and exciting RTP business for Visa.

Primary Responsibilities:

  • Build and validate predictive models with advanced machine learning techniques and tools such as Deep Neural Networks to drive business value; interpret and present modeling and analytical results to non-technical audience
  • Write and test complex predictive model software packages for production deployment; support model installations, and monitor and calibrate production models
  • Define financial and analytic metrics to measure development and production outcomes and produce visualization and reports to internal business customers
  • Propel analytical product development via conducting statistical analyses on various data sources; add values to product development by being innovative and applying the analysis results
  • Conduct transaction data analyses with Hadoop/Spark and big data tools for internal and external product owners, and develop deeper insights into the products using advanced statistical and machine learning methods

Secondary Responsibilities:

  • Support sales and marketing efforts with sound statistical and financial analysis; execute ad-hoc analyses to meet the fast-changing market demands
  • Develop business requirements and appropriate statistical analysis/prototypes to meet critical business needs
  • Derive and develop new attributes/features for modeling to grow analytic products
  • Work on cross functional teams and collaborate with internal and external stakeholders
  • Promote big data innovations and analytic education throughout the Visa organization

Qualifications

Basic Qualifications

  • 4 years of 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 degree

Preferred Qualifications

  • Advanced degree or equivalent
  • 6 years of relevant work experience with a Bachelor’s Degree or
  • 4 years of relevant work experience with a Master’s Degree or
  • 2 years of relevant work experience with a PhD
  • Experience with production model development, implementation and monitoring
  • Experience in deep artificial neural network (e.g., RNN, CNN), or graph database analytics
  • Experience in Agile development
  • Proven ability to quickly learn and apply new techniques
  • Must be a team-player and capable of handling multi-tasks in a dynamic environment
  • Excellent business writing, verbal communication, and presentation skills
  • Excellent project management skills
  • Experience with Payment Fraud modeling or Real Time Payment is a plus

 Technical Qualifications

  • Proficiency in Python data analysis and modeling
  • Proficiency in SQL
  • Proficiency in Spark
  • Experience with script and shell programming in Unix/Linux
  • Experience with using GitHub for data science projects

Additional Information

Work Hours:

This position requires the incumbent to be available during core business hours

Travel Requirements: 

This position requires the incumbent to travel for work less than 5% of the time

Mental/Physical Requirements:

This position will be performed in an office setting.  The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers, reach with hands and arms, and bend or lift up to 25 pounds. 

 

Visa will consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

 

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