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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 VP, Marketing Data Science is a highly experienced and senior role, and part of the larger Media Sciences team in the Client Org, reporting to the SVP Marketing Data Science. This role will primarily perform advanced analytics on data from the clean room, marketing data analytics, audience analytics, creative analysis, consumer journey, attribution and scenario planning. This pivotal role will be responsible for leading advanced analytics initiatives and developing sophisticated measurement models to optimize marketing spend and drive demonstrable business impact. The successful candidate will be a strategic thinker with deep expertise in data science, marketing analytics, and a proven track record of translating complex data into actionable insights for senior stakeholders.
The Media Sciences team leads the transformation of Citi’s media investment and measurement, while delivering positive return on marketing investment and future-proofing our business. The team works in close partnership with US and Global Digital Media, Analytics, Acquisitions, Advertising and Product Management teams.
Responsibilities:
- Advanced Analytics: Design, develop, and implement advanced statistical and machine learning models to analyze marketing effectiveness, cross-channel attribution, audience analytics and user journey analytics.
- Hands on implementation: Leverage multiple analytical approaches, tools and techniques, working on multiple data sources (media channel data, acquisitions data, revenue data, digital data,) A/B testing, incrementality studies addressing business questions through marketing lens
- Utilize predictive analytics to forecast marketing outcomes, identify optimal spending levels, and inform budget allocation decisions.
- Leadership and development: Translate complex business questions into analytical problems and guide the team in developing appropriate data-driven solutions.
- Stakeholder communication: Communicate complex analytical concepts and data driven insights clearly and concisely to diverse audiences, including senior leadership, marketing teams, and cross-functional partners.
- Operational Excellence: Oversee the end-to-end model development lifecycle, from data collection and preprocessing to model building, validation, and deployment.
- Accountability: Ensure the team adheres to best practices in data science, model governance, and ethical data usage and ensure timely and effective deployment of models.
- Thought Leadership: This role will serve as a subject matter expert in analytics – ability to understand complex business problems, develop analytical approaches to solve them and simplify outcomes for stakeholders. Drive ideation on analytical projects.
- Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
Qualifications:
- 6-10 years’ experience within media measurement and analytics, preferably in financial services, client side, agency side or publisher side
- Proficiency in programming languages such as Python and R, and experience with relevant data science libraries and frameworks.
- Experience with big data technologies, cloud platforms, and data visualization tools (e.g., SQL, Tableau, Power BI, Dataiku).
- Hands on experience working with complex structured and unstructured marketing data Experience leading predictive modeling using Machine Learning, Deep Learning/ Neural Networks across multiple projects
- Strong understanding of core AI/ML concepts (e.g., RNN, LSTMs, LLMs, NLP) and ability to identify the optimal solution for any business case
- Should be able to independently own and project manage workstreams involving multiple stakeholders, show strong sense of accountability and decisiveness in strong work ethic
- Must be ready for hands on involvement in measurement to identify roadblocks and uncover solutions, operate with curiosity and is hypothesis driven
- Must be process oriented and organized, operate efficiently to deliver high-quality work in a fast paced and dynamic environment across different teams
- Extensive experience working closely with legal, compliance, and procurement teams on technology contracting and approvals in a highly regulated environment.
- Proficient in Python / R, Hive / SQL
- Experience on machine learning frameworks like TensorFlow, PyTorch etc.
- Experience on Gen AI platforms and tools
Education:
- Bachelor’s/University degree (Masters or Bachelor’s degree in Business Analytics, Finance, Statistics. Advanced 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.
#LI-AB8
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Job Family Group:
Marketing -
Job Family:
Digital & Social Media Marketing -
Time Type:
Full time -
Primary Location:
New York New York United States -
Primary Location Full Time Salary Range:
$129,840.00 - $194,760.00
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
Please see the requirements listed above. -
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter. -
Anticipated Posting Close Date:
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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
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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 $129K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Citi, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. This role's midpoint ($162K) sits 23% below the category median. Disclosed range: $129K to $194K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Citi AI Hiring
Citi has 56 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Agent Developer, Data Engineer. Positions span Tampa, FL, US, Tampa, FL, Mississauga, Ontario, Canada. Compensation range: $130K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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