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About This Role
Zantech is looking for a talented Data Scientist to contribute to the success of our upcoming Technical Infrastructure and Platform Support project for an On\-Site role based out of Arlington, VA.
The Data Scientist will play a crucial role in providing:
- Data Acquisition, Analytics, and Governance Services (technical lead for all Task 3 work)
- Cybersecurity and Information Assurance (Responsible AI / AI TRiSM, RLS/CLS\-compliant model outputs)
The Data Scientist serves as the technical lead, owning the strategy, design, and deployment of advanced analytical and AI/ML models. Extracts, transforms, and analyzes complex, multi\-source data sets to generate actionable insight that informs workforce, logistics, and operational decision\-making for leadership.
Responsibilities include, but will not be limited to:
- Design, develop, and deploy predictive, prescriptive, descriptive, and cognitive AI/ML models and dashboards in collaboration with stakeholders
- Apply NLP, network analysis, and bibliometric analysis to S\&T portfolio data
- Support automated MLOps lifecycle management, including model registry, drift monitoring, and automated retraining triggers
- Ensure AI/ML outputs comply with Responsible AI (RAI) governance and inherit Row\-Level/Column\-Level Security controls
- Provide onboarding, training, and ongoing support to build a data\-literate workforce
Required Experience or Knowledge of the following technologies/functions:
- Ten (10\)\+ years developing and deploying analytical models to address S\&T or business requirements within a DoW organization
- 10 years in data science (statistical analysis, predictive modeling, machine learning, algorithm development in Python, R, and SQL)
- 10 years in data wrangling and batch/streaming pipeline development, integrating structured and unstructured sources; experience on at least 2 distinct projects applying NLP, network analysis, or bibliometric analysis to S\&T portfolio data
- 10 years using data visualization tools (Tableau, Power BI, matplotlib)
- 5 years with big data and cloud\-based analytics platforms
- 5 years applying operational analytics to workforce, logistics, or readiness missions.
- Skills Required:
- + Statistical analysis and predictive/prescriptive/descriptive/cognitive modeling
+ Python, R, SQL; ML/AI model development and MLOps lifecycle management
+ Natural Language Processing (NLP), network analysis, and bibliometric analysis of S\&T data
+ Data visualization and stakeholder communication of analytical findings
+ Federal data governance and security frameworks (NIST SP 800\-53, FISMA, DoD RMF)
Required Education/Certifications:
- Education Required:
+ Master's degree in computer science, engineering, or a related discipline, and 10\+ years of experience
- BA/BS with 12\+ years of experience is an accepted substitute,
- Education Preferred:
+ Master's or Ph.D. in Data Science, Statistics, Computer Science, or a related quantitative field
- Not specified
- Cloud ML/AI platform certification (e.g., AWS Certified Machine Learning \- Specialty, Microsoft Certified: Azure AI Engineer Associate)
- Certifications Required:
- Certifications Preferred:
Required Security Clearance:
- US Citizenship and the ability to obtain and maintain an active Secret or higher clearance, per contract requirements.
*“**Outstanding Performance…Always!”*
*Our corporate motto represents our commitment to build long\-term relationships with both our clients and our employees by providing the highest quality service in everything we do. We strive for excellence for our clients and for each other. We embrace the opportunity to hire individuals with new talents and fresh perspectives. Zantech offers competitive compensation, strong benefits, and a vacation package, as well as a fast\-paced and exciting work environment.* *Come join our team!*
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Zantech, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Zantech AI Hiring
Zantech has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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