Job DescriptionAs part of Cyber Threat Analytics and Research team (CTAR) , you will leverage cutting-edge technologies to perform statistical profiling, inference, classification, clustering and predictive analysis. As a key member of the technical team, you will create and implement sophisticated machine learning models to help derive new insights to defend against cyber-attacks. You will be working with a large variety of data sets, cutting-edge security technologies, and world-class operation teams to create awesome analytics for security and other business units.
Your responsibilities include:
- Analyze cyber event logs using Spark and big data technologies and develop deeper insights into products using advanced ML methods.
- Formulate cyber threat scenarios into technical data problems and develop high fidelity models to capture unseen threats.
- Devise and implement deep learning models for building user behavior profiles. This includes data acquisition, feature engineering, model development, and deployment.
- Conduct feature engineering on various data sources to build and enrich feature store.
- Use GenAI to auto triage anomalous user behavior by collecting all necessary contexts.
- Use GenAI to create agentic agents that enhances cyber investigation efficiency.
- As a member of the CTAR team, you will work closely with other data scientists and data engineers to build, design, engineer, and develop analytical software and services that deliver security functionality and improve security efficiency and capabilities through automation.
- Assist in shaping overall direction, life-cycle management, and leadership for Information Security architecture and technology related to Visa.
- Communicate clean and persuasive data directly to end users, leadership, and other stakeholders, technical and non-technical.
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
QualificationsBasic Qualifications
• 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience
Preferred Qualifications
• 3 or more years of work experience with a Bachelor's Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
• Less than 2 years' relevant work experience with bachelor's degree or less than 1 year relevant work experience with master's degree in data science, mathematics, statistics, economics, computer science, engineering, or other related business or quantitative discipline is required.
• Solid background and hands on experiences with building Machine learning, deep learning and AI models.
• Experience with Generative AI/LLM, AI agents.
• Excellent understanding of algorithms and data structures and proficiency in Python and SQL.
• Experience working with large datasets using tools and Hadoop, Spark, or Hive.
• Excellent analytic and problem-solving capability combined with ambition to solve hard problems.
• Strong communications skills and ability to collaborate.
• Highly driven, resourceful and results oriented.
• Good team player and excellent interpersonal skills.
• Demonstrated ability to lead and navigate through ambiguity.
U.S. Applicants OnlyThe estimated salary range for this position is $123,700.00 to $ 191,300.00 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 HoursVaries upon the needs of the department.
Travel RequirementsThis position requires travel 5-10% of the time.
Mental/Physical RequirementsThis 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.
