Sr. Data Scientist

Visa
Bengaluru
Workplace: HybridFull timeFunction: Data Science & Machine LearningExperience: 5+ yearsSkills: ["Analytical thinking","Communication","Business writing","Stakeholder management","Self-motivation"]

Build and enhance sanctions screening and name-matching models to improve AML/CFT detection accuracy and reduce false positives. Work hands-on with large-scale structured and semi-structured data, defining features and tuning rules/statistical and ML/NLP approaches. Partner with compliance, model risk management, and technology teams on validation, audits, and regulatory reviews, while documenting model logic and communicating performance insights.

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FursaFursa
Visa
Visa
6 hours ago

Sr. Data Scientist

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Source: Company careers pageValidated by: Fursa AI
Last checked: 6 hours agoStatus: Live

Job Summary

Build and enhance sanctions screening and name-matching models to improve AML/CFT detection accuracy and reduce false positives. Work hands-on with large-scale structured and semi-structured data, defining features and tuning rules/statistical and ML/NLP approaches. Partner with compliance, model risk management, and technology teams on validation, audits, and regulatory reviews, while documenting model logic and communicating performance insights.
Location: Bengaluru
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Conduct large-scale data analysis end-to-end: define analytic approaches, extract and explore data, and translate results into actionable items.
  • •Develop and enhance sanctions screening models using rules-based and fuzzy matching techniques; tune thresholds and scoring logic to improve detection and reduce false positives.
  • •Build features for matching/scoring logic and assess data quality/completeness for sanctions screening; partner with data engineering teams on sourcing and transformations.
  • •Document model logic, assumptions, and changes in an audit-ready manner; monitor model performance and implement enhancements aligned with regulatory/MRM standards.
  • •Support compliance stakeholders with hands-on querying/analysis, user acceptance testing, validation/audits, and preparation of technical documentation and model reports.

Key Requirements

  • •Bachelor’s/Master’s degree in Engineering, Economics, Statistics, Mathematics, or a related technical field.
  • •At least 5 years of experience in data analysis, reporting, statistical analysis, research, data mining, and trend analysis.
  • •At least 5 years of experience developing and implementing machine learning models using tools such as SAS, SQL, Python/R, and Hive and/or Spark.
  • •Experience building predictive/descriptive statistical models (e.g., logistic regression, random forests, SVMs, XGBoost, CNNs/RNNs) and familiarity with fuzzy matching, NLP n-grams, phonetics, tokenization, and similarity scoring.
  • •Expertise in sanctions screening and name-matching, including rules-based or statistical models and familiarity with sanctions platforms (e.g., Fircosoft, ComplyAdvantage, World-Check, Actimize).
Experience:5+ yearsAMLSanctions screeningFinancial crime complianceMachine learningNLPGenerative AICompliance technology
Education:
Skills:Analytical thinkingCommunicationBusiness writingStakeholder managementSelf-motivation
Tech Stack:Machine learningNatural language processingDeep learningGenerative AIPythonPandasNumpyMatplotlibScikitSASSQLRHiveSparkFuzzy matchingNLP n-gramsPhoneticsTokenizationSimilarity scoringLogistic regression

Company Brief

Visa
Global payments technology company that operates the VisaNet electronic payments network, facilitating credit, debit and digital transactions between consumers, merchants, financial institutions and governments worldwide.
Industry: Payments
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 1958
Glassdoor
Glassdoor: 3.8
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