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About This Role
Description
JOB TITLE:
Security Data Science Specialist
DEPT/DIV:
Security
SUPERVISOR:
Director, Data Info \& Analysis
WORK LOCATION:
2 Broadway, New York, NY 10004
HOURS OF WORK:
8:00 am \- 4:30 pm (7\.5 hours/day) or as required
FULL/PART\-TIME
FULL
SALARY RANGE:
$89,404 \- $100,582
DEADLINE:
Until filled
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.
Opening:
The Metropolitan Transportation Authority is North America's largest transportation network, serving a population of 15\.3 million people across a 5,000\-square\-mile travel area surrounding New York City, Long Island, southeastern New York State, and Connecticut. The MTA network comprises the nation’s largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. MTA strives to provide a safe and reliable commute, excellent customer service, and rewarding opportunities.
Job Summary:
The security data science specialist within MTA HQ's Office of Security will be tasked with collecting and utilizing highly sensitive police and security data, information, and intelligence sets from all MTA Police and Security operating departments. This will include incident reports, surveillance footage, threat assessments, and criminal records. This data will be meticulously compiled, validated, and analyzed to identify critical security trends, emerging threats, and operational vulnerabilities, thereby supporting a comprehensive, risk\-based approach to all MTA and Security Operations.
The primary duties of the incumbent will be focused on data gathering, transformation, synchronization, and modeling. This work will require experience in languages such as SQL and Python, as well as supporting the design and deployment of robust data architecture and analytics strategies tailored to diverse datasets and complex analytical challenges. Responsibilities include working on detailed record\-keeping processes, conducting advanced data analysis, preparing comprehensive reports, and developing actionable, data\-driven strategies aligned with worldwide, regional, and local security, law enforcement, terrorism, and crime prevention initiatives.
The role requires a detail\-oriented, proactive approach, with the ability to manage assigned priorities under strict deadlines while providing excellent communication skills to present findings to both technical and nontechnical audiences.
Responsibilities:* Writing code that will combine and transform datasets to meet a specific Security \& Police need.
- Design and implement quality controls to ensure output data is valid, accurate, and user\-friendly. Coordinating with internal and external police and/or security data leads to addressing data quality problems. Enhance the quality and usability of legacy datasets and systems that may be lacking in compatibility or common field types.
- Developing reports and dashboards in a variety of formats (Excel, Power BI, Report Builder, ArcGIS, automated emails, etc.) to share finished analysis with stakeholders.
- Lead specific data team projects/recurring tasks such as monitoring, analyzing, synthesizing, and reporting on confidential and sensitive open\-source and police, intelligence, and security information sources to inform the overall MTA security strategy.
- Utilize internal systems, tools, and processes to record and track MTA security incident data to identify trends and patterns. Suggest methods to ensure data integrity and improve data collection, processes, and presentation.
- Collaborate with stakeholders and leadership to understand reporting needs and design outputs to achieve them. Produce the final design and workflows in consultation with senior managers. Outline project requirements and problems as action plans that can be assigned to more junior team members. Ensure project process and outputs are properly documented for continuity and presentation to Senior Security Management.
- Providing support and instruction (in both business knowledge and coding practices) to analysts and consultant staff.
- Keeping skills current by learning new algorithms, programming languages, and techniques.
Required Knowledge/Skills/Abilities:* Proficiency in data management, including knowledge of statistics, quantitative and qualitative data analysis, empirical research methods and procedures such as sampling and surveying techniques, and analytical skills. Additionally, they will have the capability to stay current with technical innovation and trends in data science.
- Experience in developing and analyzing algorithms, dashboards, and/or predictive models to support security and/or policing functions and investigative methodologies.
- Proven ability to manage team\-based projects, completing both short\-term and long\-term initiatives efficiently and effectively. Experience in documenting processes and quality checks.
- Strong skills in database design and management with the ability to read code and interpret data.
- Demonstrated proficiency with data processing, statistical software and management support tools, including Microsoft Office Suite or comparable applications, advanced Excel analysis, and business intelligence tools (e.g., Power BI, Tableau).
- Experience in supervising staff performing analytical duties.
- Familiarity with transportation systems and planning theory \& practice, particularly the MTA subway, bus, and railroad networks.
Required Education and Experience:* Bachelor’s Degree in Arts/Sciences (BA/BS) in Security, Criminal Justice, Homeland Security, Mathematics, Psychology, Sociology, Public Administration, Computer Science, Engineering, Information Management, Statistics, Data Science, Data Engineering, Mathematics, Public Policy or a related field from an accredited college or an equivalent combination of education from an accredited college and experience may be considered in lieu of a degree.
- Minimum 3 years of professional experience in data, information, and analysis or performing statistical analysis and research, analyzing data, developing recommendations, implementing strategies, and preparing reports with similar programming and data management content. A master’s degree may substitute for one year of experience.
- Minimum 3 years of professional experience building datasets, automating tasks through scripts, writing database queries, and debugging/ maintaining code.
- Minimum 4 years of experience with Python and SQL programming.
- Minimum 3 years of experience with relational databases (e.g., Oracle, Postgres, SQL Server), including writing queries (generally with PL/SQL) to obtain and manipulate data.
- A valid driver's license is required.
The Following is/are preferred:* Master’s degree in Arts/Sciences (MA/MS) in Security, Criminal Justice, Homeland Security, Mathematics, Psychology, Sociology, Public Administration, Computer Science, Engineering, Information Management, Statistics, Data Science, Data Engineering, Mathematics, Public Policy or a related field from an accredited college. A Master's degree can account for one year of related experience.
- Demonstrated experience meeting report filing deadlines for analysis reports with recommendations, strategy implementations, and statistical findings at an executive level, interpersonal, organizational, and presentation skills with the ability to think at a policy and strategic level.
- A working knowledge of project management principles and/or data analysis, predictive analytics, and experience in their application is desirable.
- A working knowledge and understanding of the following are highly desirable: Federal, State, and NYC security and penal regulations and laws.
- Familiarity with the MTA’s policies and procedures.
- Familiarity with the MTA’s collective bargaining procedures.
- Familiarity with data exploration/data visualization tools like Tableau, Power BI, Web Focus, etc.
- Familiarity with GIS mapping of data or equivalent geospatial application.
Other Information
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.
According 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 concerning 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 $89K-$100K 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 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 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($94K) sits 48% below the category median. Disclosed range: $89K to $100K.
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.
Metropolitan Transportation Authority AI Hiring
Metropolitan Transportation Authority has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $100K - $134K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 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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