Interested in this Data Scientist role at Metropolitan Transportation Authority?
Apply Now →Skills & Technologies
About This Role
Description
JOB TITLE:
Senior Data Scientist
DEPT/DIV:
Strategic Initiatives / Data \& Analytics
SUPERVISOR:
Director Data Science
WORK LOCATION:
2 Broadway, New York, NY 10004
HOURS OF WORK:
9:00 am \- 5:30 pm (7\.5 hours/day) or as required
FULL/PART\-TIME
FULL
SALARY RANGE:
$120,000 \- $134,110
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 incumbent in this position will help lead Data \& Analytics design and implementation, data and programming strategies that are applicable to a wide range of datasets and different types of analytical problems. They will use languages such as SQL, Python, and R and relational database tools such as Oracle, Postgres, and SQL Server to analyze large datasets, build new ones, and design overall data architectures. The incumbent must have expertise, or the ability to quickly understand and build expertise, in different areas of the MTA to support assumptions and business reviews. The incumbent will need to carefully document their work and be able to explain it clearly in response to questions from all levels of the company. They will design all projects and work outputs to support strategic goals to build data systems and processes that are well\-structured and sustainable. Due to the size and complexity of the projects they will be working on, the incumbent will also be required to collaborate closely with operators, data/ process owners, IT, and their own colleagues in Data \& Analytics to achieve successful outcomes.
Responsibilities:
- Designing data structures and designing and writing code to collect, combine, and transform datasets to meet business needs.
- Designing and carrying out quality controls on output data for validity, accuracy, and usability by the desired audience.
- Working with business partners to understand reporting needs, design appropriate procedures and outputs to achieve them, and carry out other associated work.
- Breaking down large requests/ problems from senior managers into discrete and solvable programming tasks.
- Documenting work in a thorough manner consistent with team standards so that it can be easily understood by teammates.
- Creating documents explaining the work done – both the outputs/ insights and the pipeline – for technical and non\-technical audiences.
- Performing statistical analyses on a wide range of datasets.
- Keeping skills current by learning new algorithms, programming languages, and techniques.
- Providing support and instruction (in both business knowledge and coding practices) to less\-experienced members of the team.
- Other duties as assigned.
Required Knowledge/Skills/Abilities:
- Strong skills in programming for data analytics, most preferably in Python, but other languages such as R and Java are valuable.
- Strong skills in database design and management
- Understanding of analytical methods (e.g., probability and statistics, algorithm design).
- Familiarity with transit/ transportation systems, particularly the MTA subway, bus, and railroad networks.
- Exceptional ability to read code and interpret data.
- Familiarity with data processing and management support tools, including MS Office, advanced Excel analysis, and business intelligence tools (e.g., Power BI, Tableau).
- Familiarity with transportation planning theory and practice, especially in large\-scale transit systems.
- Ability to collaborate and provide support to all levels of MTA, both technical and non\-technical.
- Ability to project manage and help lead team\-based projects.
- Proficiency in data management.
- Experience in documenting processes, as well as performing quality checks.
- Ability to keep up with technical innovation and trends in data science.
- Familiarity with KPI metrics and the ability to create algorithms to calculate them.
- Ability to deconstruct difficult problems into smaller and simpler pieces.
- Ability to think at a policy and strategic level
- Strong written communication skills.
Required Education and Experience:
- Bachelor’s degree in Computer Science, Engineering, Information Management, Statistics, Mathematics, or Transportation Planning. An equivalent combination of education and experience may be considered in lieu of a degree.
- A minimum of (3\) years of experience in data science or data engineering, or another position with similar programming and data management content. A Master’s degree may substitute for one year of experience.
- A minimum of (3\) years of experience building datasets, automating tasks through scripts, writing database queries, and debugging/ maintaining code.
- A minimum of (4\) years of experience with Python and SQL programming.
- Minimum of (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.
The Following is/are preferred:
- Bachelor's degree in Computer Science, Engineering, Information Management, Statistics, Mathematics, or Transportation Planning/ Civil Engineering.
- Ability to write, edit, and understand Python code, SQL, or R Programming.
- Familiarity with data exploration/data visualization tools like Tableau, Power BI, Web Focus, etc.
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 $120K-$134K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Metropolitan Transportation Authority, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 36% below the category median. Disclosed range: $120K to $134K.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.