Citi

Senior Data Scientist Vice President

Citi
US San Antonio, TX, US
Onsite 2026-07-02
Announced salary
$113,840 - $170,760
Low
$70K
Median
$92K
High
$122K
Market in San Antonio · BLS OEWS 2025
Estimated net pay
$7,410 - $10,665
/month · 22% withheld
after tax & contributions · Single, no dependents

Job description

**Discover your future at Citi** -------------------------------- Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact. **Job Overview** ---------------- The Business Analytics Lead Analyst is a member of the Fusion Analytics Team under FFC. This role will be tasked with creating analytical solutions across all LOB’s utilizing statistical and advanced analytical techniques and drive strategic insights and decision making. An ideal candidate will have strong expertise in machine learning, predictive modeling and financial data analysis. Experience in digital fraud detection and cybersecurity will be a strong advantage. You will need to collaborate with cross\-functional teams to develop and implement data driven solutions. Key responsibilities include: * Role requires 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. Additionally, 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 d

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