Staff Data Scientist

6 months ago

Bengaluru, India Visa Full time

Company Description

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across 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.

When you join Visa, you join a culture of purpose and belonging - where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world - helping unlock financial access to enable the future of money movement.

**Join Visa: A Network Working for Everyone.**

**Job Description**:
Payments are a very exciting and fast-developing area with a lot of new and innovative ideas coming to market. With strong demand for new solutions in this space, it promises to be an exciting area of innovation. VISA is a strong leader in the payment industry and is rapidly transitioning into a technology company with significant investments in this area.

If you want to be in the exciting payment space, learn fast and make big impacts, Ecosystem & Operation Risk technology which is part of Visa’s Value-Added Services business unit is an ideal place for you

This position is ideal for an experienced data scientist who is passionate about collaborating with business and technology partners in solving challenging fraud prevention problems. You will be a key driver in the effort to define the shared strategic vision for the Payment Fraud Disruption platform and defining tools and services that safeguard Visa’s payment systems.

**Essential Functions**
- As a Data Scientist you will help design, enhance, and build next generation fraud detection solutions in an agile development environment.
- Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product stakeholders.
- Work with software engineers to ensure feasibility 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, 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.
- Mentor and train other data scientists on the team on key solutions
- Able to work on multiple projects and initiatives with different/competing timelines and demands.
- Present technical solutions, capabilities, considerations, and features in business terms. Effectively communicate status, issues, and risks in a precise and timely manner
- Collaborate across engineering teams and leaders in Ecosystem& Operational Risk, Visa Research, AI Platform, Operations, and Infrastructure (O&I), security and platform teams.

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:
5+ years of relevant 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, OR 8+ years of relevant work experience.
Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks
Expert in leading-edge areas such as Machine Learning, Deep Learning, Stream Computing and MLOps
Ability to program in one or more scripting languages such as Perl or Python and programming languages such as Java or Scala
Excellent understanding of algorithms and data structures.
Excellent analytic and problem-solving capability combined with ambition to solve real-world problems
Excellent interpersonal, facilitation, and effective communication skills (both written and verbal) and the ability to present complex ideas in a clear, concise way
Have great work ethi

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