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About This Role
ESG Controllers – Sustainability Reporting Policy & Governance Lead
About the ESG Controllers Team
The ESG Controllers team within Finance oversees the strategy, preparation, review and submission of mandatory ESG/sustainability disclosures for Citigroup Inc. (“Citi”). The team partners closely with Sustainability & ESG, Risk, Finance, Legal, HR and other global functions to deliver accurate, timely and well‑controlled disclosures. Team’s remit includes establishing consistent global standards, strengthening disclosure controls and data lineage, enhancing reporting transparency, and driving continuous improvements across processes, metrics, and the technology enablement that supports Citi’s sustainability reporting obligations.
Role Summary
The SVP, Sustainability Reporting Policy & Governance Lead defines, drives and embeds Citi’s global sustainability reporting policy framework, governance model and disclosure control environment—with an emphasis on influencing senior stakeholders and supporting critical decision‑making as Citi builds the core foundational frameworks and policies to meet regulatory obligations for the Group and International Legal Entities. The role provides enterprise leadership across policy design, regulatory interpretation, executive engagement, regulatory‑grade execution, and global coordination.
Key Responsibilities
Policy, Standards & Methodology
- Draft, implement and maintain policy, standards and procedures that define global sustainability reporting requirements (governance, roles/responsibilities, materiality, controls), aligned to various global sustainability reporting standards like CSRD/ESRS, ISSB/IFRS S1 & S2, TCFD, GRI, etc.
- Integrate sustainability reporting processes into the Enterprise Risk Management (ERM) Framework and taxonomy; strengthen Disclosure Controls & Procedures (DC\&P) tailored for ESG.
Regulatory Change Management & Thought Leadership
- Provide authoritative viewpoints on emerging sustainability disclosure regulations; lead the translation of regulatory changes into Citi’s policy framework, operating procedures and control environment.
Group–International Legal Entities Alignment & Global Coordination
- Oversee alignment of Group and legal‑entity ESG disclosures, partnering with international finance/regulatory reporting teams to ensure consistency of narrative, data and controls while meeting local requirements.
Executive Governance & Decision Support
- Run cross‑functional executive steering forums for effective challenge and oversight; manage escalation pathways and decision logs.
- Develop and deliver Board/management committee materials, distilling complex regulatory themes, disclosure judgments and control status into clear, actionable narratives.
Leadership & Talent
- Lead and coach a high‑performing team of VPs/AVPs and SMEs; build succession depth, role clarity and a culture of accountability, collaboration and proactive risk management.
Qualifications & Experience
- 10+ years in financial/regulatory/ESG reporting, risk governance or related disciplines within financial services or Big 4 consulting firms; 5+ years leading managers/teams.
- Deep familiarity with ESRS, ISSB, TCFD, and GRI standards; ability to interpret complex regulatory requirements and operationalize them across a large, matrixed organization.
- Proven ability to navigate a large organization, synthesize multiple information points and build repeatable processes that standardize how information is presented and governed.
- Outstanding interpersonal, communication and presentation skills with executive presence; capable of influencing senior stakeholders and driving alignment across diverse groups.
- Strong strategic mindset and technical problem‑solving skills; adept at identifying conflicts/discrepancies and assembling the right cross‑functional team to solution them.
- Bachelor's/University degree in Finance, Accounting or related field; CPA or MBA preferred.
Core Competencies & Behaviors
- Executive influence & decision facilitation; concise storytelling for senior management committees.
- Controls orientation and risk judgment; strong partnership with Risk & Controls.
- Program leadership in complex, global, multi‑stakeholder environments; continuous improvement mindset.
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Job Family Group:
Finance
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Job Family:
Regulatory Reporting
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Time Type:
Full time
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Primary Location:
New York New York United States
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Primary Location Full Time Salary Range:
$163,600.00 - $245,400.00
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
Business Acumen, Change Management, Communication, Data Analysis, Financial Acumen, Internal Controls, Issue Management, Problem Solving, Regulatory Reporting.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
Feb 04, 2026
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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.*
*If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review* *Accessibility at Citi**.*
*View Citi’s* *EEO Policy Statement* *and the* *Know Your Rights* *poster.*
Salary Context
This $163K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $210,000 based on 1,345 positions with disclosed compensation. Disclosed range: $163K to $245K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Citi AI Hiring
Citi has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Tampa, FL, US, New York, NY, US, Boca Raton, FL, US. Compensation range: $155K - $245K.
Location Context
AI roles in New York pay a median of $223,400 across 228 tracked positions.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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