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About This Role
Discover your future at Citi
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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
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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 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.
- Collaborate with fraud analytics modelling function to understand new fraud detection capabilities, develop new analytical solutions leveraging unstructured data sets and variables.
Job skills/ Qualifications
- Bachelor’s degree in engineering, Statistics, Economics, Finance, Mathematics or a related quantitative field from a premier institute required. (Master's degree not required but beneficial)
- 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.
- Ability to analyze large \-scale, unstructured data and generate actionable insights
- 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.
- Successful candidate will have a demonstrable analytic, problem solving, and leadership skills, and has the ability to deliver projects in a fast\-paced environment.
- 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
- Solutions\-oriented “can do” attitude, with ability to drive innovation via thought leadership while maintaining end\-to\-end view.
- Extremely detail\-oriented, with strong, intellectual curiosity. Ability to effectively multi\-task and work in a fast\-paced and evolving environment, while setting meeting high standards
\#LI\-BS1
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Job Family Group:
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Decision Management
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Job Family:
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Business Analysis
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Time Type:
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Full time
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Primary Location:
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San Antonio Texas United States
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Primary Location Full Time Salary Range:
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$113,840\.00 \- $170,760\.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental \& vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Most Relevant Skills
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Please see the requirements listed above.
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Other Relevant Skills
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For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
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*Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.*
Salary Context
This $113K-$170K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Citi, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. This role's midpoint ($142K) sits 26% below the category median. Disclosed range: $113K to $170K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citi AI Hiring
Citi has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Jersey City, NJ, US, Tampa, FL, US. Compensation range: $160K - $500K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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