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About This Role
The AI Governance Senior Analyst is responsible for helping develop and implement AI governance practices to promote secure, compliant, and ethical use of artificial intelligence throughout the organization.
In this position, you will help steer NYPA’s AI governance efforts by creating and applying robust frameworks that follow current standards, while supporting ongoing innovation within AI projects.
Key responsibilities include shaping and maintaining authority\-wide AI policies and standards in collaboration with cross‑functional stakeholders.
You will:
- Develop the AI governance framework, focusing on data privacy, security, reducing bias, promoting transparency and accountability, and ensuring compliance with regulations.
- Facilitate discussions on AI governance with stakeholders
- Stay abreast of advancements in AI technology and governance best practices
The ideal candidate can work seamlessly across all levels of the organization, collaborating with business, technology, cybersecurity, compliance, and data governance partners to balance business needs with governance, security, and data protection requirements.
They are comfortable navigating ambiguity and excel at creating structure in new, evolving spaces. As this is a newly created role, the candidate will help shape foundational processes, frameworks, and operating models aligned with emerging AI policies, governance principles, and risk‑management practices.
Responsibilities
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- Participate in the formulation, development, revision, and implementation of Authority AI policies and standards of practice, directly in cooperation with various stakeholders.
- Maintain and implement the department’s AI governance framework that addresses data privacy, security, bias mitigation, transparency, accountability, and regulatory compliance.
- Analyze and evaluate artificial intelligence usage and requests in the department and make recommendations for improvements and optimization.
- Maintain AI system inventory / registry/ models through AI Governance Tools/Technology
- Manage projects focused on governance, risk, and control assessments to ensure data it AI ready
- Collaborate with data engineers, software developers, and other IT professionals to ensure proper implementation of AI models and to integrate AI governance principles into all stages of AI lifecycle management
- Manage the development and operations of the Data Catalog, Business Glossary and various Data Governance \& Quality processes.
- Lead training, communication and enterprise\-wide education on Data Governance \& Quality best practices, and the enablement of new policies, procedures \& frameworks.
- Collaborate with business units to understand their data needs and identify data requirements including business rules/controls, metrics, and targets for various datasets.
Knowledge, Skills and Abilities
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- Working knowledge of Data Quality tools such as Trillium, Ataccama, or other such tools.
- Working knowledge of Data Governance, Data Quality and Data Lineage concepts.
- Proficiency in programming Languages including Structured Query Language (SQL), Python (or other scripted language), and ETL frameworks.
- Strong understanding and hands\-on experience of Data Governance \& Quality domains such as metadata management, master data management, data quality, data stewardship and/or data protection.
- Strong communication skills with the ability to interact with diverse audience across business and technical teams, and the confidence to present to senior leaders.
- Strong organizational skills with the ability to prioritize and execute tasks across multiple projects with tight deadlines and aggressive goals.
Education, Experience and Certifications
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- Bachelor's degree in related technical field required, Master's degree preferred.
- Minimum of 4 years of data\-related work experience including at least 2 years as an analyst or lead within a data governance \& quality function.
Physical Requirements
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- Approximately 5% travel primarily within NY State
The New York Power Authority is committed to providing fair, competitive, and market\-informed compensation. The estimated salary range for this position is: $117,000 \- $146,000\. The salary offered will be determined based on the successful candidates’ relevant experience, knowledge, skills, and abilities.
The New York Power Authority and Canal Corporation believes that diversity, equity, and inclusion drive our success, and we encourage women, people of color, LGBTQIA\+ individuals, people with disabilities, members of ethnic minorities, foreign\-born residents and veterans to apply. As an equal opportunity employer, NYPA/Canals is committed to building inclusive, innovative work environments with employees who reflect communities across New York and enthusiastically serve them. We proudly celebrate diversity and do not discriminate based on race/color, creed/religion, national origin, citizenship or immigration status, age, disability, military status, gender/sex, sexual orientation, gender identity/expression, pregnancy and related conditions, familial/marital status, domestic violence victim status, predisposing genetic characteristics, arrest/criminal conviction record or any other category protected by law.
NYPA/Canals will also provide reasonable accommodations during the hiring process related to candidates’ disabilities, pregnancy\-related conditions, religious observances/practices and/or domestic violence concerns. To request an accommodation, please email [email protected].
Salary Context
This $117K-$146K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At New York Power Authority, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($131K) sits 27% below the category median. Disclosed range: $117K to $146K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
New York Power Authority AI Hiring
New York Power Authority has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in White Plains, NY, US. Compensation range: $146K - $146K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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