Vista previa de la oferta
Sr. Data Scientist
senior · Data Science & Analytics
About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
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 Sanctions Screening. Additionally, experience in techniques including fuzzy matching, NLP based n-grams methodology, phonetics, tokenization, and similarity scoring will be desirable.
The candidate is expected to demonstrate expertise in sanctions screening and name-matching, along with experience working with rules-based or statistical models and familiarity with major sanctions platforms such as Fircosoft, ComplyAdvantage, World-Check, and Actimize, etc. will be preferable.
The candidate will be expected to have understanding of global sanctions regimes, including OFAC, EU, UN, and UK guidelines. They should demonstrate strong knowledge of Anti-Money Laundering (AML) and Counter Financing of Terrorism (CFT) frameworks, as well as financial crime compliance practices.
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 approaches, 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
- Analyze structured and semi structured data (names, addresses, identifiers). Assess data quality, completeness, and appropriateness for sanctions screening. Define and test features used in matching and scoring logic. Partner with data engineering teams on data sourcing and transformations.
- Build and enhance sanctions screening models using rules based and fuzzy matching techniques. Tune thresholds and scoring logic to improve detection accuracy and reduce false positives. Analyze screening data and support feature design to strengthen match quality.
- Document model logic, assumptions, and changes in an audit ready manner. Monitor model performance and implement enhancements in line with regulatory and MRM standards. Design and conduct experiments to evaluate the model performance using appropriate metrics and statistical analysis to measure and interpret results
- Partner with Compliance, Model Risk Management, and Technology teams to support testing, validation, audits, and regulatory reviews. 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
Bachelor's/Master’s Degree in Engineering, Economics, Statistics, Mathematics, or related technical discipline
∙ 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
∙ Building predictive and descriptive statistical models using AI/ML algorithms (e.g., logistic regression, random forests, SVMs, XGBoost, CNNs/RNNs). Additionally, experience in techniques including fuzzy matching, NLP based n-grams methodology, phonetics, tokenization, and similarity scoring will be desirable.
∙ Expertise in sanctions screening and name-matching, along with experience working with rules-based or statistical models and familiarity with major sanctions platforms
∙ Excellent knowledge of database management, data extraction and data manipulation skills
∙ Superior communication, business writing and stakeholder management skills
∙ Self-motivated and proactive in talking to business partners/clients to identify and understand problem statements
∙ Passionate about building end-user experiences that deliver measurable value without increasing complexity
∙ Willing to work on a flexible schedule across different time zones
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.