Senior Data Scientist - Fraud
MercurySalary not specified
Full Time
Senior (5 to 8 years)
Not SpecifiedCompensation
Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Financial Services, Payments, FintechIndustries
Requirements
- Strong ML and Data Science background with demonstrated experience in building, training, implementing, and optimizing advanced AI models for payments, risk, or fraud prevention products that created business value and delivered impact in payments or similar industries
- Strong academic track record
- Excellent software engineering skills
- Ability to engage in high bandwidth conversations with business and technology partners
- Ability to think broadly about Visa’s business and drive solutions to enhance payment ecosystem safety and integrity
- Self-starter comfortable with ambiguity
- Strong attention to detail
- Excellent collaboration skills
- Passionate about collaborating with business and technology partners to solve challenging fraud prevention problems
Responsibilities
- Lead the design and production deployment of new and advanced ML techniques to detect and prevent cybercrime, including point-of-sale (card present/card not present) terminal fraud, anomalous cross-border volumes, Purchase Return Authorization fraud, data manipulation attacks, and ATM Cashouts
- Manage fraud alerting taxonomies and thresholds that optimize true positive detections and fraud scheme coverage
- Collaborate with Technology partners to enhance fraud detection capabilities for improved insights and pattern detection
- Build and experiment with modeling and scoring algorithms, including development of custom algorithms and use of packaged tools based on machine learning, data mining, and statistical techniques
- Get to the root of problems, define business objectives, and formalize data requirements, especially in fast-paced, fluid environments
- Develop real-time and batch processed customized monitoring solutions to enable clients to take proactive action against fraud threats
- Analyze complex financial data to uncover patterns, anomalies, and insights that support risk assessment and fraud identification
- Engage with VCA to understand client-specific risk and fraud challenges, providing expert guidance and tailoring solutions to their needs
Skills
Machine Learning
Data Science
AI Models
Fraud Detection
Anomaly Detection
Python
SQL
Model Training
Model Optimization
Payments Risk
Visa
Global digital payment network provider
About Visa
Visa operates a global digital payment network that facilitates electronic payments for millions of people daily. The company connects consumers, businesses, financial institutions, and governments, allowing them to make transactions using Visa cards. Each time a card is used, Visa earns money through transaction, service, and data processing fees. Unlike many competitors, Visa focuses on expanding access to financial services for underserved communities and supporting local economies. The company's goal is to promote financial inclusivity and drive sustainable commerce, ensuring that more people can participate in the global economy.
San Francisco, CaliforniaHeadquarters
1958Year Founded
$55.8MTotal Funding
ANGEL_INDIVIDUALCompany Stage
Fintech, Financial ServicesIndustries
10,001+Employees
Benefits
Health Insurance.
Life Insurance.
Dental Insurance.
Disability Insurance.
Accidental Death & Dismemberment Insurance.
Risks
CBDCs could reduce reliance on Visa's payment network.
'Buy Now, Pay Later' services may decrease traditional credit card transactions.
Fintech startups offering zero-fee transactions could pressure Visa's revenue model.
Differentiation
Visa operates a global digital payment network connecting millions daily.
The company focuses on financial inclusivity and sustainability in its operations.
Visa collaborates with central banks on Central Bank Digital Currencies (CBDCs).
Upsides
Visa's partnership with fintechs enhances cross-border payment solutions.
Adoption of blockchain technology could revolutionize digital payments.
AI-driven fraud detection systems are reducing fraudulent activities.
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