Sr. Data Scientist

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 Senior Data Scientist position requires an experienced professional who is passionate about data analysis, data modeling using machine learning and product development to help us build and maintain statistical & ML/AI based solutions in the AML & Sanctions Compliance space, specifically supporting the Transaction Monitoring and Client Due Diligence programs. The candidate should be a motivated self-starter and quick learner willing to work in a high-pressure fast paced environment and globally located teams

The candidate will be extensively involved in hands-on querying, analyzing, proposing and implementing optimization solutions, conduct user acceptance testing, and support business stakeholders’ enquiries on ad hoc basis.  Successful candidate will be working in the Compliance Technology Operations (CTO) team which is part of the Global Ethics and Compliance organization

Essential Functions:

• Strong analytical skills, with the ability to conduct large scale data analysis through all stages: understand business goal and strategy, formulate analytic approach, perform data extraction and exploration, and translate results into actionable items

• Expertise in AI techniques (Machine learning, natural language processing, deep learning), including exposure to Generative AI technologies and their applications. Experience with Python libraries such as pandas, numpy, matplot and Scikit

• Deployment of high-performance scalable AI/machine learning (ML) algorithms for building transaction monitoring and risk scoring solutions to improve the quality of alerts and investigation process

• Conduct periodic tuning of machine learning models using large-scale datasets to optimize models for performance, efficiency, and robustness

• Design and conduct experiments to evaluate the model performance using appropriate metrics and statistical analysis to measure and interpret results

• Supervise and work with the Model Risk Management team as an independent contributor to support model validations across multiple business entities. Ensure the accuracy, reliability, and compliance of models by overseeing and conducting thorough validations

• Collaborate with internal stakeholders across business units and regulated entities to enhance alignment in implementing MRM policy/standards and promote MRM best practices

• Prepare technical documentation, including model architecture, implementation details and model methodology. Review model reports and communicate findings, insights, and recommendations to both technology and business stakeholders

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Qualifications

Basic Qualification
∙Bachelor's/Master’s Degree in Engineering, Economics, Statistics, Mathematics,
or related technical discipline

Preferred Qualification
∙At least 5 years of experience in data analysis, reporting, statistical analysis,
research, data mining, trend analysis etc.
∙At least 5 years of experience in machine learning model development and
implementation using tools like SAS, SQL, Python/R , Hive and/or Spark

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.