Staff Machine Learning Scientist (8+ years exp) Full-time

Company Description

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 Staff ML Scientist will work with a team to conduct world-class Applied AI research on data analytics and contribute to the long-term research agenda in large-scale data analytics and machine learning, as well as deliver innovative technologies and insights to Visa’s strategic products and business. This role represents an exciting opportunity to make key contributions to Visa’s strategic vision as a world-leading data-driven company. 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.

Essential Functions

• Formulate business problems as technical data problems while ensuring key business drivers are collected in collaboration product stakeholders.
• Work with product engineering to ensure implement-ability of 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 adaptive learning with controls on efficiency, methods for explaining model decisions where vital, model validation, A/B testing of models.
• 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.

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:
• 8 or more years of work experience with a Bachelors Degree or with an Advanced Degree.
• Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
• Ability to program in one or more scripting languages such as Perl or Python and one or more programming languages such as Java, C++, or C#.
• Experience with one or more common statistical tools such SAS, R, KNIME,.
• Deep learning experience with TensorFlow is a plus.
• Experience with Natural Language Processing is a plus.
• Experience working with large datasets using tools like Hadoop, MapReduce, Pig, or Hive is a must.
• Publications or presentation in recognized Machine Learning and Data Mining journals/conferences 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.