Senior Data Scientist

Citi

Senior Data Scientist at Citi analyzing fraud patterns and providing strategic insights through data-driven solutions. Collaborating with cross-functional teams using advanced analytics to mitigate financial losses.

Posted 6/30/2026full-timeSan Antonio • Florida, Texas • 🇺🇸 United StatesSenior💰 $113,840 - $170,760 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data analysis, statistical modeling, and machine learning techniques to identify and mitigate fraud risks. Proficient in leveraging AI/ML frameworks and advanced data visualization tools to provide actionable insights and enhance fraud detection capabilities.

Highest-signal resume keywords

Python ProgrammingStatistical AnalysisAI/ML FrameworksData VisualizationFraud Detection Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisData MiningPredictive ModelingStatistical SoftwareMachine Learning Techniques

Soft Skills

Quantitative SkillsAnalytic SkillsCollaboration

Tools & Technologies

SQLSASTableauHadoopTeradata

Industry Keywords

Fraud DetectionRisk ManagementCybersecurityDark Web MonitoringFraud Intelligence

Tech Stack

Tools & technologies

Cyber SecurityHadoopPythonPyTorchRDBMSScikit-LearnSQLTableauTensorflow

About the role

Key responsibilities & impact

creating analytical solutions to mitigate losses across all LOB’s utilizing various statistical/advanced data science techniques
analysis of compromised cards data from dark web to identify emerging fraud trends, detect suspicious fraud patterns, and anomalies, identify high risk merchants, locations and transaction behaviors
leverage AI/ML models (eg anomaly detection, graph neural networks, NLP) to anticipate fraudulent behavior
experience with AI techniques and implement AI powered automation to improve fraud detection efficiency
develop and enhance data models and algorithms to identify high risk accounts for proactive monitoring and closure
assess the impact of compromised cards on fraud losses, use statistical analysis to quantify risk exposure and identify abnormal spending patterns
collaborate with threat intelligence teams to incorporate external fraud signals into risk models
identify fraud rings, mule accounts, synthetic identities by linking compromised data to existing customer portfolios
generate executive level insights and reports for leadership team using advanced visualization techniques and provide regular updates on fraud trends and emerging threats
provide actionable insights to senior global stakeholders by leveraging data analytics and reporting
lead POC’s with new vendors, evaluating fraud detection tolls, data enrichment platforms and dark web monitoring solutions
perform ad-hoc analysis on large, unstructured datasets (eg transaction logs, dark web feeds) to identify fraud indicators
use Python, SQL and SAS to extract, transform and analyze complex datasets
manage significant fraud events by helping coordinate information sharing across financial crime and fraud prevention Org
partner with various cross-functional teams such as Fraud Policy, Analytics & Modelling, Security Operations Center to help design intelligence derived solutions to detect fraud

Requirements

What you’ll need

Bachelor’s degree in engineering, Statistics, Economics, Finance, Mathematics or a related quantitative field from a premier institute required
Minimum 5+ relevant experience in data analysis, data mining, or statistical analysis
Must have a working knowledge of Python, SQL, Teradata, RDBMS, Hadoop/Hive Tools
Experience in statistical analysis with working knowledge of at least one of the following statistical software packages: Python (Preferred), SQL, SAS (required)
Experience with AI/ML Frameworks (eg TensorFlow, PyTorch , Scikit-learn)
Knowledge of Large language models (LLM’s) for text analysis and Fraud Intelligence
Experience in Predictive modeling, statistical analysis and machine learning techniques
Familiarity with digital fraud detection tools and experience working with cybersecurity datasets and threat intelligence platforms
Prior experience in developing dynamic dashboards using visualization tools such as Tableau
Data Science work in any risk domain will be preferable
Experience in identifying fraud patterns in large consumer banking portfolios
Excellent quantitative and analytic skills and data-driven mindset; ability to derive patterns, trends and insights, and perform risk/reward trade-off; Ability to effectively collaborate with cross-functional partners and management

Benefits

Comp & perks

medical, dental & vision coverage
401(k)
life, accident, and disability insurance
wellness programs
planned time off (vacation)
unplanned time off (sick leave)
paid holidays