Staff Research Scientist

Job Description Visa Research conducts applied and fundamental research and was created to lead the company in pushing the boundaries of payment related technologies on a global scale. We engage with the company's numerous technology and product teams, world‑wide business partners, academics, and governments to explore and develop technologies that will shape the future of the payments industry and impact the lives of billions of people. The role will work with a growing team to advance the long‑term research agenda in artificial intelligence and machine learning to extract actionable insights from transaction data, 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 a strong academic track record and demonstrate excellent software engineering skills. The candidate will be a self‑starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills. Responsibilities Lead research projects in the area of generative AI, scalable AI, and foundation models Develop innovative approaches and models to address complex problems in generative AI Collaborate with a multidisciplinary team to design and implement scalable AI solutions Mentor and guide junior researchers, providing technical expertise and guidance Stay abreast of the latest research trends, publications, and advancements in the field Publish research findings in top‑tier conferences and journals Collaborate with industry partners and academic institutions to foster research collaborations Contribute to the intellectual property portfolio through patent filings Set strategic direction for research initiatives, aligning them with organizational goals and business priorities Evaluate emerging technologies and recommend adoption strategies for Visa’s products and services This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. Qualifications Basic Qualifications PhD in Computer Science, Operations Research, Statistics, or a highly quantitative field with a strength in deep learning, machine learning, data analytics, statistical or other mathematical analysis Extensive experience in machine learning, deep learning, and generative models Strong publication record in top‑tier conferences and journals Preferred Qualifications Proficiency in programming languages such as Python and expertise in deep learning frameworks and large language models Proven track record of leading research projects and delivering innovative solutions Demonstrated ability to work collaboratively in a team Experience in payments or a related industry is a plus U.S. Applicants Only Salary and Benefits The estimated salary range for this position is $169,100.00 to $270,800.00 USD per year, which may include potential sales incentive payments if applicable. Salary may vary depending on job‑related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity. Visa has a comprehensive benefits package for which this position may be eligible that includes medical, dental, vision, 401(k), FSA/HSA, life insurance, paid time off, and wellness program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5‑10% 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. 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 the EEOC guidelines and applicable local law. #J-18808-Ljbffr