Data Scientist - Data Product Development (PhD)
- Austin, TX, USA
Everyone at Visa works with one goal in mind – making sure that Visa is the best way to pay and be paid, for everyone everywhere. This is our global vision and the common purpose that unites the entire Visa team. As a global payments technology company, tech is at the heart of what we do: Our VisaNet network processes over 13,000 transactions per second for people and businesses around the world, enabling them to use digital currency instead of cash and checks.
We are also global advocates for financial inclusion, working with partners around the world to help those who lack access to financial services join the global economy. Visa’s sponsorships, including the Olympics and FIFA™ World Cup, celebrate teamwork, diversity, and excellence throughout the world. If you have a passion to make a difference in the lives of people around the world, Visa offers an uncommon opportunity to build a strong, thriving career. Visa is fueled by our team of talented employees who continuously raise the bar on delivering the convenience and security of digital currency to people all over the world. Join our team and find out how Visa is everywhere you want to be.
Visa customers trust us with the richest data on earth about global commerce. Working on data at Visa is unique opportunity at a time when the payments industry is undergoing a digital transformation with data as a critical differentiator.
We offer you the opportunity to be at the center of innovation in the payments industry and unleash the power of data through applying data sciences to business problems.
As a data scientist based in Austin, this position is responsible developing and delivering predictive analytic capabilities that get incorporated into Visa products in a variety of domain such as Risk & Fraud, Commercial and Merchant areas.
This role will be part of a group that works in tight collaboration with product engineering, product management, and operations to ensure business effectiveness of products.
We desire candidates with deep expertise in machine learning or statistics and experience in delivering predictive systems on big data.
The following are the group responsibilities:
Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with Risk, Commercial and Merchant product management.
Work with product development (engineering) to ensure implementability of solutions. Deliver prototypes and production code based on need.
Work with Data platform to drive availability of relevant data, tools, and infrastructure for group for experimental and development purposes.
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, data mining and statistical techniques.
Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.
Devise and implement methods for efficiently monitoring model effectiveness 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
The responsibilities above are group responsibilities and specific individuals will be assigned responsibilities based on the group's needs and individual skills and preference Qualifications.
Recent graduate in PhD in Computer Science, Operations Research, Statistics or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.
Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks.
Deep learning experience with TensorFlow is a plus.
Strong understanding of algorithms and data structures.
Strong analytic and problem solving capability combined with ambition to solve real-world problems.
Results orientation with ability to plan work and work in a team
Strong verbal and written communication skills.
Experience working with large datasets using tools like Hadoop, MapReduce, Pig, or Hive is a plus.
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, Matlab.
Publications or presentation in recognized Machine Learning and Data Mining journals/conferences is a plus.
All your information will be kept confidential according to EEO guidelines.