Interested in this Data Scientist role at E Logic?
Apply Now →Skills & Technologies
About This Role
Location: Remote (Primary place of performance is remote; occasional on-site support may be required at TSA Headquarters, 6595 Springfield Center Drive, Springfield, VA 22150)
Clearance: Must meet minimum DHS SUITABILITY requirements for Entry on Duty (EOD); U.S. Citizenship required
Job Type: Full-time
Contract Type: Government Contract (Base Year: 30 September 2026 - 29 September 2027; two 12-month option periods)
Position Overview
Seeking a skilled Data Scientist to provide data analysis, reporting, and compliance analytics support to the Department of Homeland Security (DHS), Office of the Chief Information Officer (OCIO), Experience and Accessibility Division (EAD). This role supports the EAD's mission to ensure that Information and Communications Technology (ICT) procured, developed, maintained, or used by DHS is accessible (Section 508) to employees and customers with disabilities.
This is a non-personal services contract position. The contractor employee reports directly to the Prime Contractor for HR, disciplinary, and administrative matters, while receiving work direction from the Government Section 508 Program Manager and the Contracting Officer's Representative (COR).
This is a remote position with occasional on-site requirements as directed by the Government.
Key Responsibilities
- Identify appropriate information resources, gather data, analyze, validate, and produce reports to support internal and external reporting requirements, including but not limited to:
- Annual OMB and GSA Governmentwide Section 508 assessment
- Quarterly DHS Component Section 508 Program Health Assessments
- Annual HQ OCIO Program Health Assessment
- Review and interpret web accessibility test results based on automated scans
- Compare automated test rule sets and remediation guidance to DHS compliance testing methodologies
- Conduct trend analysis and produce reports of findings and recommendations tailored to multiple audiences (including executives, Section 508 Program Managers, and web teams)
- Compile and produce monthly, quarterly, and annual activity and compliance reporting information using Accessibility Compliance Management System (ACMS), a customized ticketing system based on the ServiceNow platform
- Prepare optional topic-specific reporting as determined by DHS senior leadership
- Support the development of a strong data model and scoring approach that can be used to measure performance against the requirements in the SOW
- Analyze data related to Section 508 evaluation and remediation, electronic document remediation, ACR/VPAT reviews, accessibility technical assistance, training development, outreach, helpdesk support, testing, and multimedia accessibility assistance
- Provide data-driven insights to support the EAD's strategic direction and decision-making
- Create and maintain technical documentation (SOPs, user guides) and draft user communications regarding data findings and updates
- Provide clerical and administrative assistance to the CIO3 IT Office as needed
- Perform other data-related duties as assigned
Required Qualifications
- Education: Bachelor's degree in Data Science, Statistics, Computer Science, Information Systems, or a related field (or equivalent work experience)
- Experience: Minimum of three (3) years of experience in data analysis, data science, or a related field
- Citizenship: U.S. Citizenship required
- Security: Must meet minimum DHS SUITABILITY requirements for Entry on Duty (EOD) unless otherwise indicated
- Language: Fluent in written and verbal English
- Technical Skills:
- Proficiency in data analysis tools and programming languages (e.g., Python, R, SQL)
- Experience with data visualization tools (e.g., Tableau, Power BI)
- Experience with statistical analysis and modeling
- Proficiency in Microsoft Office Suite (Word, Excel, PowerPoint, Outlook, Teams)
- Analytical Skills: Strong analytical and problem-solving skills with the ability to interpret complex data sets and produce actionable insights
Preferred Qualifications
- Experience with Section 508 accessibility data and compliance reporting
- Experience with Accessibility Compliance Management System (ACMS) or similar ticketing systems (ServiceNow platform)
- Experience with automated web accessibility testing tools (e.g., axe, WAVE, SiteImprove)
- Experience with government reporting requirements (OMB, GSA, DHS)
- Experience with data governance and data quality management
- Familiarity with the Revised Section 508 Standards and WCAG 2.0 Level A and AA Success Criteria
- Experience creating dashboards and executive-level reports
Work Role Information
- Work Role: Data Scientist - Accessibility Compliance Analytics
- Proficiency Level: Intermediate to Senior
Additional Requirements
- Must comply with all DHS security regulations and safeguard Controlled Unclassified Information (CUI)
- Must pass background checks and suitability determinations
- Must complete DHS Information Technology Security Awareness Training annually
- Must sign and comply with DHS Rules of Behavior
- Must maintain any required certifications
Schedule & Work Conditions
- Hours: Up to 40 hours per week, Monday-Friday, normal business hours (7:30 AM - 6:00 PM EST), excluding Federal holidays
- Overtime: Only permitted at the Government's discretion
- Telework: Remote; occasional on-site support may be required as directed by the Government
- Travel: Not authorized
- Place of Performance: Remote (primary); TSA Headquarters, Springfield, VA (if on-site support is requested)
Equal Opportunity Employer
We are an equal opportunity employer and do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At E Logic, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
E Logic AI Hiring
E Logic has 2 open AI roles right now. They're hiring across Data Scientist. Positions span San Diego, CA, US, Washington, DC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.