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About This Role
Data Scientist
General Information
Requisition \#715
Locations USA\-VA\-Arlington
Posting Date 06/12/2026
Security Clearance Required \- IRS MBI
Remote Type Hybrid
Time Type Full time
Description \& Requirements
Elder Research Inc., a wholly owned subsidiary of MANTECH international Corporation seeks a motivated, career and customer\-oriented Data Scientist to join our team in Arlington, VA. This role is a remote role preferably in the Washington DC area.
As a Data Scientist, you will support the Internal Revenue Service’s mission to improve tax compliance, fraud detection, and risk identification across large, complex tax and financial data environments. You will work directly with government clients, program managers, and technical teams to understand business and compliance challenges, design analytical approaches, and deliver data\-driven solutions that inform enforcement, audit prioritization, and fraud prevention efforts.
In this role, you will develop, test, and deploy predictive and statistical models using structured and unstructured data to identify anomalies, non\-compliance risk, and potential fraud within tax records and related datasets. You will contribute across the full data science lifecycle, from problem formulation and data exploration through model validation, deployment, and stakeholder communication.
Responsibilities include but are not limited to:
- Prior programming experience, preferably in Python or R, including data exploration, feature engineering, model development, and writing modular, reusable, well\-documented code within an iterative development process that includes peer review and collaboration
- Demonstrated experience using Python, SQL, and Databricks for data analysis, modeling, statistical evaluation, and working with Markdown for technical documentation
- Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science–driven solutions in support of assessments, gap analyses, and actionable recommendations for IRS stakeholders
- Design, develop, test, validate, and implement quantitative and qualitative data science solutions and predictive risk models (including audit selection, refund review, and fraud prevention initiatives) that are modular, maintainable, adaptable to evolving government and regulatory requirements, and supported by robustness, sensitivity, and significance testing to ensure defensible and explainable results
- Apply statistical and machine learning techniques (supervised and unsupervised) to anomaly detection, fraud identification, and non\-compliance risk scoring to help prioritize cases based on compliance risk, fraud indicators, and business impact
- Collaborate with clients, subject matter experts, and cross\-functional teams to refine problem statements, requirements, and analytical approaches, while demonstrating the ability to work independently in a collaborative, fast\-paced environment
- Prepare and deliver technical and non\-technical briefings, reports, and presentations to audiences with varying levels of analytical sophistication, translating business and compliance needs into technical solutions with strong interpersonal, written, and verbal communication skills
Minimum Qualifications:
- Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences
- 2\-10\+ years of experience in data science, analytics, or a related technical field
- Experience using version control systems (e.g., Git) and collaborative development practices
- Strong understanding of relational databases and SQL
- Comfortable learning new tools, methodologies, and domains, including working outside your comfort zone
- Strong analytical mindset with a willingness to tackle complex mathematical and statistical challenges
- Willingness to travel and work on\-site at client locations as required by project needs
Preferred Qualifications:
- Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields; experience applying advanced statistical concepts including sampling considerations, bias detection, weighting techniques, handling missing or outlier data, exploratory analysis, and longitudinal forecasting; and understanding of the data analytics lifecycle (e.g., CRISP\-DM)
- Experience with PySpark, Unity Catalog, and Jobs in Databricks, with familiarity using platforms and tools such as Databricks and AWS
- Experience with Natural Language Processing (NLP) and text analytics applied to unstructured documents or case notes, as well as graph analytics and network analysis to identify relationships, fraud rings, or interconnected entities
- Experience with containerization and environment management (e.g., venv, conda)
- Experience operating in secure or remote government environments, including use of bash and command\-line tools
Clearance Requirements:
- Must currently possess an IRS Public Trust clearance with Full Background Investigation
Physical Requirements:
- Must be able to remain in a stationary position 50%
- Needs to occasionally move about inside the office to access file cabinets, office machinery, etc.
- Frequently communicates with co\-workers, management, and customers, which may involve delivering presentations. Must be able to exchange accurate information in these situation
About Elder Research, Inc \- People Centered. Data Driven
*Elder Research considers all qualified applicants for employment without regard to disability or veteran status or any other status protected under any federal, state, or local law or regulation.*
*If you need a reasonable accommodation to apply for a position with Elder Research, please email us at [email protected] and provide your name and contact information.*
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 Elder Research Inc, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Elder Research Inc AI Hiring
Elder Research Inc has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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
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