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About This Role
Description
JOB TITLE: Senior Manager of Security Data Science
DEPT/DIV: Security
SUPERVISOR: Director, Data Information \& 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 Time
SALARY RANGE: $110,233 \- $124,012
DEADLINE: Open until filled
- This position is eligible for teleworking, which is currently one day per week. New hires are eligible to apply for telework 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 Senior Manager of Security Data Science at MTA HQ's Office of Security will play a lead role in overseeing the collection and management of highly sensitive police and security data, information, and intelligence from all MTA Police and Security operating departments. This role involves directing the development of the data strategy, data processes, and program structure for Police and Security, as well as analyzing and presenting findings to leadership. These findings include incident reports, surveillance footage, threat assessments, and criminal records. The data will be carefully gathered, validated, and analyzed to detect critical security trends, emerging threats, and operational vulnerabilities, supporting a comprehensive, risk\-based approach to all MTA and Security Operations. The person will also act as the proxy when the Director is unavailable.
The main responsibilities involve data collection, transformation, synchronization, and modeling. This role demands considerable experience with SQL and Python, as well as the ability to direct the design and implementation of robust data architecture and analytics strategies for diverse datasets and complex analytical tasks. Key duties include maintaining 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 incumbent will manage projects and staff to ensure successful outcomes.
Responsibilities:
- Managing the Creation of data structures and developing code to collect, combine, and transform Security datasets for business needs.
- Lead data structure 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 address data quality problems. Enhance quality and usability of legacy datasets and systems that may be lacking in compatibility or common field types.
- Act as the internal expert for the Security data science team and supervise more junior staff members to monitor, analyze, synthesize, and report on confidential and sensitive open\-source and police, intelligence, and security information sources to inform the overall MTA security strategy.
- Manage internal systems, tools, and processes to record and track MTA security incident data to identify trends and patterns. Work with agency security staff to ensure data integrity and improve data collection and presentation. Collaborate with the MTA agencies, MTA Police Department, New York Police Department, Federal and local law enforcement agencies’ data teams, to gather, present, and share security data for purposes of crime prevention and counter terrorism.
- Collaborate with Security \& Police departments to identify reporting needs, develop appropriate processes to achieve them, and deliver associated tasks.
- Independently define project requirements and problems as action plans that can be assigned across the team to ensure workload balancing. Oversee and ensure project processes and outputs are properly documented for continuity and presentation to Senior Security Management.
- Select, develop, oversee, and motivate personnel within the department. Provide career development for subordinates. Provide prompt and effective coaching and counseling. Responsible for discipline/termination of employees when necessary. Review performance of staff. Create a professional environment that respects individual differences and enables all employees to develop and contribute to their full potential.
- Performs other duties as assigned
- Complies with all policies and standards
- May be required to work hours outside regular work hours, as applicable
- Observes the work performed by contractors, as applicable
- Reviews invoices and approves them if the work meets contractual standards, as applicable
- Addresses performance issues with the contractor when possible, as applicable
- Escalates issues to other parties when needed, as applicable
Required Knowledge/Skills/Abilities:
- Proficiency \& subject matter expert in data management, including knowledge of statistics, conducting quantitative and qualitative data analysis, and applying empirical research methods and procedures such as sampling and surveying techniques, as well as analytical skills. Additionally, they will be able to stay current with technical innovations and data science trends. Incumbent is expected to be able to lead and provide training to subordinates within these skills and abilities.
- Experienced in developing and analyzing algorithms, dashboards, and/or predictive models to support security and/or policing functions and investigative methodologies. This requires the incumbent to lead a team in utilizing these methodologies for project management as it pertains to Police \& Security data analytics.
- The candidate must have experience in meeting deadlines for submitting analysis reports that contain recommendations, strategy implementations, and statistical findings aimed at an executive audience. The ideal candidate will also have strong interpersonal, organizational, and presentation skills, as well as the ability to think strategically and at a policy level.
- Proven ability to effectively communicate and perform in a high\-profile, high\-pressure environment, both in writing and verbally, and interacting well with all levels of the organization as well as external agencies such as the Governor’s Office for New York State, New York City government, public officials, and staff at federal or state agencies or authorities.
- Must have strong supervisory skills to effectively direct professional staff and technical employees in implementing the short\- and long\-term goals and direction for their areas of responsibility, e.g., monitoring staff's daily performance and project progress.
- Proven ability to manage and lead team\-based projects, completing both short\-term and long\-term initiatives efficiently and effectively. Experience in documenting processes and quality checks.
- 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).
- Strong skills in leading database designs and management with the ability to read code and interpret data.
- Familiarity with transportation systems and planning theory \& practice, particularly the MTA subway, bus and railroad networks.
- Proven supervisory experience in 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.
- Experience in supervising staff performing analytical duties.
- Experience with data exploration/data visualization tools like Tableau, Power BI, Web Focus, etc.
- Experience with GIS mapping of data or equivalent geospatial application.
Required Education and Experience:
- Minimum 8 years of 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 8 years of experience in building datasets, automating tasks through scripts, writing database queries, and debugging/ maintaining code.
- 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.
- Minimum 4 years of programming experience for data analytics, most preferably in Python \& SQL, but other languages such as R and Java are valuable.
- Minimum 2 years of of Supervisory/leadership experience.
- Valid Driver's License
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 $110K-$124K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($117K) sits 46% below the category median. Disclosed range: $110K to $124K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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: $124K - $124K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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