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Requirements
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MOS Code: 16K1S (Air Force)
Education and Experience: Bachelor's Degree or higher in Data Science, Mathematics, Computer Science, Economics, Statistics or related field, plus four (4\) years of experience in database management and analysis, program evaluation, process optimization, public finance, project management, or economic analysis; including two (2\) years of supervisory experience in a management or administrative capacity.
Licenses and Certificates: Texas Class "C" Driver's License or equivalent from another state.
General Purpose
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Under general direction, develop, coordinate, and lead in identifying and integrating datasets to find opportunities for services and process optimization using models to test the effectiveness of recommended interventions.
Typical Duties
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Examine and analyze data from databases to drive optimization and improvement of services provided by the city and related strategies. Involves: Work with stakeholders throughout the organization to identify opportunities for leveraging internal and external data to develop business solutions. Assess the effectiveness and accuracy of new data sources and data gathering techniques. Develop custom data models and algorithms to apply to data sets. Coordinate with various cross\-functional teams to implement models and monitor outcomes. Develop processes and tools to monitor and analyze model performance and data accuracy. Develop and maintain databases to facilitate internal and external reporting.
Develop and manage relational databases to meet obligatory set of data required by funding agencies. Involves: Communicate analytic solutions to stakeholders and recommend improvements as needed to operational systems. Coordinate with internal and external entities to collect, manage, and analyze data; verify information for completeness and accuracy. Interpret data relative to the city's operations and budget; highlight issues and make recommendations to department heads and city officials as needed; respond to and assist department heads and stakeholders on related issues. Develop financial and economic databases; document revenue sources; develop an estimate methodology; work with departmental fiscal staff to improve their estimating capabilities and focus on revenues to encourage greater accountability. Develop and produce ad\-hoc reports, dashboards and analysis to assist in leadership decision making. Train on best practices regarding data analysis, database management, statistical methodology, data extraction, and development of interactive online reporting systems. Provide oversight and participate in meetings regarding the city's bond programs as needed; forecast revenue sources and advise on debt structure; prepare and deliver presentations on the city's economy and finances in disclosure documents for the city's bond issues.
Supervise assigned personnel. Involves: Schedule, assign and assess work. Appraise employee performance and review evaluations by subordinate supervisors. Provide for training and development. Enforce personnel rules and regulations and work behavior standards firmly and impartially. Counsel, motivate and maintain harmony. Interview applicants. Recommend hiring, termination, transfers, discipline, and merit pay or other employee status changes.
General Information
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To view complete job specification, click here.
Note: This is an unclassified contract position.
Note: Applicants are encouraged to apply immediately. This position will close when a preset number of qualified applications have been received.
Note: Applicants with a foreign degree or diploma must have all documents translated and evaluated by an agency of the National Association of Credential Evaluation Services (NACES prior to submitting them to the Human Resources Department. Please visit www.naces.org/members for more information.
A résumé and/or other documents will not be accepted in lieu of a completed application. Comments such as “See résumé” are not acceptable and will result in the application being considered incomplete.
To qualify for this position, the required education, experience, knowledge, and skills must be clearly stated on your application’s employment history. We do not use any information on your resume to review if you meet the minimum qualifications for this position.
Failure to fully detail all experience and job duties in the application, or copying/pasting directly from the job specification, or responses referring to your résumé will eliminate you from consideration for the position.
Salary Context
This $76K-$95K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At City of El Paso, 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 in Demand for This Role
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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($86K) sits 55% below the category median. Disclosed range: $76K to $95K.
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.
City of El Paso AI Hiring
City of El Paso has 1 open AI role right now. They're hiring across Data Scientist. Based in El Paso, TX, US. Compensation range: $95K - $95K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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).
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 3,708 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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