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About This Role
Description:
The role of the journey level Data Scientist is to lead the analysis of programmatic and technical data as it is collected from various program and/or project offices. The Data Scientist will be responsible for working with a multi-network data management specialist to organize and analyze data against predetermined metrics. This person will be responsible for utilizing artificial intelligence (AI) models (CAPRA and or GenAI) to analyze data in support of the proposed gap analyses and business case analyses. Their skillset must include prompt engineering for AI models, data analysis, predictive modeling, and producing concise data summaries.
Responsibilities and Duties
- Coordinate with program and project offices to gather, clean, and integrate technical and programmatic data from multiple sources.
- Work closely with the multi-network data management specialist to structure, maintain, and ensure the integrity of large, complex datasets.
- Analyze data against predetermined metrics to assess sustainment performance, readiness, and capability gaps.
- Apply artificial intelligence and machine learning models (e.g., CAPRA, GenAI) to support gap analyses and business case analyses.
- Develop and refine prompts to optimize the performance and relevance of AI-generated outputs for project needs.
- Build and validate predictive models to forecast sustainment outcomes, readiness trends, and resource requirements.
- Create clear, concise data visualizations and summaries to communicate findings to engineers, logisticians, and program managers.
- Produce technical reports and actionable data summaries that support decision-making and project objectives.
- Collaborate with cross-functional teams, including systems engineers, logisticians, and program managers, to ensure data-driven insights are actionable and aligned with project goals.
Requirements:
Minimum Qualifications:
- Top Secret Clearance with SAP eligibility Required
- Demonstrated experience delivering results in support of multi-level security projects.
- Accredited B.S. degree relevant to data science or analytics.
- Demonstrated experience in data analysis, predictive modeling, or applied statistics in a technical or programmatic environment.
- Hands-on experience utilizing artificial intelligence and machine learning models, including prompt engineering for GenAI or similar tools.
Desired Qualifications (minimum qualifications plus the following):
- Direct experience supporting ACAT-level programs.
- Advanced degree in a relevant discipline.
- Direct experience working with defense, aerospace, or government programmatic/technical data.
- Experience with Python, R, SQL, or similar languages for data manipulation and analysis.
- Experience automating data workflows and processes using scripting or workflow automation tools.
ACE Group is a Service-Disabled Veteran Owned Small Business that provides expertise in the operational application of current and future airborne weapons systems, with an emphasis on their lethality and survivability in the Battlespace. Our employees draw from a wealth of personal, professional and company experience to provide our Nation’s decision makers with the knowledge required to optimally equip our Armed Services with affordable, lethal and survivable weapons systems. We serve as our customers’ most trusted agents in their goal of producing enterprise-wide survivability investments and executable programs. We fill key roles within customer offices, offering critical knowledge regarding design, development, procurement, and test and evaluation of new and upgraded weapons and platforms. ACE Group’s philosophy is simple: Our success must be grounded on service and honest relationships. We focus on meeting the needs of our customers and employees, and trust that with those satisfied employees and satisfied customers, the business will take care of itself. ACE Group provides exciting opportunities for professional growth, and generous benefits, including personal time off, individual and family health, vision, and dental benefits, and 401(k) .
ACE Consulting Group is an Equal Opportunity Employer, including individuals with disabilities and protected veterans.
Salary Context
This $130K-$165K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Air Combat Effectiveness Consulting Group, LLC, 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 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($147K) sits 22% below the category median. Disclosed range: $130K to $165K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Air Combat Effectiveness Consulting Group, LLC AI Hiring
Air Combat Effectiveness Consulting Group, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in Lexington Park, MD, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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