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Description
JOB TITLE:
Planner, Climate \& Data Science
AGENCY:
Construction \& Development
DEPT/DIV:
Planning\\Capital Strategy
REPORTS TO:
Manager, Climate Resilience Planning
WORK LOCATION:
2 Broadway
HOURS OF WORK:
8:30 AM to 5:00 PM or as required (7\.5 HR/ DAY)
JOB FAMILY: DAB
GRADE: 003
SALARY RANGE:
$77,059 to $96,324
DEADLINE:
Open Until Filled
May need to work outside of normal work hours (i.e., evenings and weekends)
Travel may be required to other MTA locations or other external sites.
MTA Construction \& Development reserves the right to remove this posting before the application deadline.
Agency Mission
MTA Construction \& Development (C\&D) is responsible for the planning, development, and execution of all capital construction projects across the MTA region. Through centralized oversight and management of the MTA’s $64 billion Capital Program, C\&D ensures the efficient delivery of critical infrastructure projects that support the region’s vast transportation network. As part of the Metropolitan Transportation Authority, the largest transportation network in North America, C\&D plays a key role in modernizing and expanding services that support 15\.3 million residents across New York City, Long Island, southeastern New York State, and Connecticut. C\&D is committed to delivering safe, innovative, and cost\-effective capital projects that enhance the customer experience and strengthen the reliability and resilience of the MTA system.
Telework
This position is eligible for telework, which is currently one day per week. New hires are eligible to apply 30 days after their effective date of hire.
Job Summary
The Planner, Climate \& Data Science will support the implementation of priority climate resilience initiatives in the context of the MTA’s multi\-billion\-dollar 5\-year Capital Plan and the development of future climate resilience initiatives for subsequent Capital programs. This Planner will support project management and analytical planning functions, including the development and evaluation of climate investment strategies, and work with MTA stakeholders to advance critical capital initiatives to further the climate resilience of the MTA’s assets and infrastructure. This position will focus on MTA’s regional railroad assets, including Metro North Railroad, Long Island Railroad, and Staten Island Railway. For more information on MTA’s climate programs, visit new.mta.info/climate.
Responsibilities
The Planner, Climate \& Data Science will support the development and implementation of climate resilience priorities and practices as it relates the MTA’s capital assets.
The Planner will also track internal progress on programs and initiatives that make the New York City region and the MTA itself more resilient to climate change.
The Planner, Climate \& Data Science will contribute substantially to all, or a subset therein, of the following core Planning activities:
Execution of the MTA’s Climate Resilience Roadmap and 2025\-2029 5\-year Capital Plan, and development of the upcoming Twenty\-Year Needs Assessment and future 5\-year Capital Program, particularly in relation to climate resilience.
Support senior staff in the development and planning of multiple climate resilience proposals and initiatives. This includes reviewing scopes, budgets, and work plans for various projects.
Review consultant deliverables and facilitate coordination of feedback amongst internal MTA stakeholders.
Facilitate the development and use of databases for tracking information, including climate risk and impact data, completed climate protections and investments, asset assessments, asset inventories, regional planning information, transit usage, and socio\-economic and demographic information to inform prioritization with the MTA’s capital strategies.
Use data visualization tools, including GIS and dashboards, to analyze and visualize contexts and implications of transportation improvements and vulnerabilities to climate risk.
Perform quantitative and qualitative analyses.
Research and quantify the benefits of climate resilience investments, including models to estimate the magnitude of avoided losses through proactive planning and design requirements.
Development of clear reports, maps, charts, graphics, and presentations for both internal and external purposes.
Track and map impacts of severe weather events on MTA assets to inform senior staff and support development of mitigations recommendations.
Education and Experience
Bachelor's degree from an accredited college in Urban Planning, Engineering, Public Administration, Earth or Environmental Sciences, Geography, Public Policy, Transportation, or a related field.
Must have a minimum of three (3\) years of satisfactory full\-time professional experience.
Master's degree preferred.
Understanding of and fluency with climate modeling, resilience initiatives, and risk\-based decision\-making.
Proficiency in climate policies, data analytics, visualization tools, and spatial analysis.
Experience with project planning and management.
Passion for improving New York’s public transit system.
Knowledge of the MTA regional network and operating context, as well as local, state, and federal transportation and policies.
Effective communication, written, verbal, and presentation skills.
Demonstrated ability to work with all internal levels within a given organization.
Comprehensive Benefits \& Total Rewards Package:
Transportation \& Financial Benefits
Commuting Made Easy – Enjoy a complimentary MTA transportation pass, plus access to tax\-advantaged commuter benefits to maximize your savings.
Premium Health Coverage at Low Cost – Access high\-quality individual, family, and domestic partner healthcare, dental, vision, and life insurance plans.
Secure Your Future – Build long\-term financial security through pension plans and retirement savings accounts designed for eligible employees.
Time Off \& Work\-Life Balance
Generous Time Away – Recharge with substantial paid time off and comprehensive holiday schedules that support your personal and family commitments.
Holistic Support Services – Access our dedicated Work Life Services team and Office of the Chaplains unit for personal guidance and support when you need it most.
Professional Growth \& Development
Learning \& Development Program – Advance your career through structured professional development opportunities, skills training, and leadership programs tailored to support your growth within the organization.
Educational Investment – Pursue your career goals with in\-house training and professional development, tuition reimbursement support, and partnerships with educational institutions.
Employee Experience \& Community
Employee Assistance Programs – Comprehensive support services to help you navigate life's challenges with confidence and resources.
Discounts \& Perks – Take advantage of MTA employee discount programs offering savings on products and services.
Connect \& Belong – Join our vibrant Employee Resource Groups to build meaningful connections, share experiences, and contribute to an inclusive workplace culture.
How to Apply
For Internal Applicants: Log in to the My MTA Portal, click on the My Job Search tile, select the Careers link, search for the desired position, click Apply, and follow the on\-screen instructions.
For External Applicants: Visit www.mta.info, click the “Careers” link located in the footer under the "The MTA" section, then click on “See All Open MTA Positions”. Search for the desired position, click Apply, and follow the instructions.
Additional Information:
Final salary is determined by experience, skill set, and alignment with compensation practices. The posted range reflects expected compensation and may be updated as market or business needs evolve.
To be eligible for consideration for a new role, current MTA employees must complete at least one year of service in their current role prior to applying. Additionally, eligibility to interview is contingent upon maintaining a satisfactory record of job performance, attendance, and disciplinary conduct.
Pursuant to the New York State Public Officers Law \& the MTA Code of Ethics, all employees who hold a policymaking position must file an Annual Statement of Financial Disclosure (FDS) with the NYS Commission on Ethics and Lobbying in Government (the “Commission”).
Equal Employment Opportunity
MTA and its subsidiary and affiliated agencies are equal opportunity employers, including those with respect to veteran status and individuals with disabilities.
The MTA encourages qualified applicants from diverse backgrounds, experiences, and abilities, including military service members, to apply.
Salary Context
This $77K-$96K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Metropolitan Transportation 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($86K) sits 60% below the category median. Disclosed range: $77K to $96K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Metropolitan Transportation Authority AI Hiring
Metropolitan Transportation Authority has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $96K - $96K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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