Interested in this Data Scientist role at Citi?
Apply Now →Skills & Technologies
About This Role
Discover your future at Citi
Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 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 AML Data Scientist will work under the supervision of a Functional Team Manager to provide ongoing support in terms of required analysis. The scope of analysis will cover all aspects of analysis performed by the team within different projects: Key Performance Indicator Driven Tuning and Tactical Tuning tasks, and data modeling/validation efforts depending on current needs and project plans.
The Data Scientist will follow the globally consistent methodology but is expected to have a high level of initiative and creativity and suggest enhancements to current methodologies. The analyst evaluates moderately complex and variable issues with substantial potential impact, where development of an approach/taking an action involves weighing various alternatives and balancing potentially conflicting situations using multiple sources of information. It requires good analytical skills to filter, prioritize and validate potentially complex and dynamic material from multiple sources. Strong communication skills are required. Work and performance of all teams in the area are directly affected by the performance of the individual.
Responsibilities:
- Apply quantitative and qualitative data analysis methods including Python (and R), SQL to extract, transform and analyze data.
- Prepare statistical and non-statistical data for visualization and exploration, validate data, identify data quality issues in AML risk related projects.
- Contribute towards AML Optimization techniques and related deliverables i.e. Optimizing monitoring systems, document optimization solutions, and present results to non-technical audiences; write formal documentation using statistical vocabulary.
- Build Machine Learning (Predictive Modeling/ Classification/ Regression) models.
- Conduct data analysis, data mining, read and create formal statistical documentation, and work with Technology and Sponsors to address issues.
- Analyze and interpret data reports, make recommendations addressing business needs.
- Validate assumptions, escalate identified risks and sensitive areas in methodology and process.
- Automate recurring data extraction and data pre-processing tasks, perform ad-hoc data analyses, and provide documentation and presentations.
- Appropriately assess risk when business decisions are made keeping in mind the firm's reputation by being compliant with applicable laws, rules and regulations and adhering to Policy.
- Apply sound ethical judgment regarding personal behavior, conduct and business practices, and escalate, manage and report control issues with transparency.
Qualifications:
- Strong statistical/ data science related academic background, knowledge of quantitative methods, including predictive modeling techniques and including both calculation and interpretation of outcome.
- Experience in creating and assessing Statistical Models.
- Good working knowledge of Python, SQL & MS Office Suite.
- Experience in reporting the results of analysis in clear written form, and in presenting the findings during meetings and conference calls.
- Team working experience (demonstrated team player ability required).
- Understanding of technical requirements, ability to communicate with Technical Support.
- Ability to manipulate data using SQL or equivalent.
Desirable:
- Experience with data exploration.
- Mantas and AML monitoring systems experience or other transaction monitoring systems a plus.
- Knowledge of the local/regional laws and regulations applicable to anti-money laundering a plus.
- Previous experience with financial services companies (retail banking, small business banking, commercial, institutional, private banking).
- Knowledge of any programming language or dedicated statistical software would be an advantage.
Skills:
- Initiative, creativity and attention to detail are essential.
- Ability to work under close supervision of a functional team manager, while preserving independence of thinking and responsibility for the results.
- Ability to discuss reasoning, critical assessment of facts and opinions; ability of giving comprehensive answers and arguments.
- Conceptual and creative thinking (outside the box).
- Demonstrated ability to communicate effectively, both orally and in writing, through all levels of the organization and through different business units.
- Self-motivated with high desire of self-development and learning.
- Must be able to coordinate and deal with conflicting time demands.
Ability to work non-standard business hours, including weekends and holidays as determined by the business needs. Flexibility will be expected. *
Education:
- Bachelors (4 Year University) Degree in Statistics, Mathematics, Data Science, Data Analytics, Business Analytics, or related quantitative fields required.
- Masters Degree preferred.
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
-
Job Family Group:
Technology -
Job Family:
Data Science -
Time Type:
Full time -
Primary Location:
Tampa Florida United States -
Primary Location Full Time Salary Range:
$87,280.00 - $130,920.00
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.
-
Most Relevant Skills
Please see the requirements listed above. -
Other Relevant Skills
Analytics, Anti-Money Laundering (AML), Anti-Money Laundering Compliance, Data Analysis, Data Analytics, Data Development, DataPro, Data Science, Microsoft Excel, Microsoft Office, Python (Programming Language), Python for Data Analysis, Python Frameworks, Statistical Analysis, Statistical Coding, Statistical Data, Statistical Testing. -
Anticipated Posting Close Date:
Oct 20, 2026 -
Automated Processing and AI
We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.
Illinois residents – AI Notice and Right
-
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 $87K-$130K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% 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 $190,000 based on 1,503 positions with disclosed compensation. This role's midpoint ($109K) sits 43% below the category median. Disclosed range: $87K to $130K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Citi AI Hiring
Citi has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist, AI Software Engineer. Positions span Jersey City, NJ, US, Jersey City, NJ, New York, NY. Compensation range: $130K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.