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About This Role
Everforth ECS is seeking a *Data Scientist Lead* to join our team in *Tampa, FL.*
The successful candidate will be a senior subject matter expert in data science, leading a team of data professionals to enhance the command's data\-driven decision\-making capabilities. This role requires a demonstratable history of designing, developing, and operationalizing advanced analytical solutions within Military, Federal, or Intelligence organizations.
The Data Scientist Lead will provide oversight for the Data Management team and drive the implementation of strategic initiatives in data engineering, data science, and Artificial Intelligence/Machine Learning (AI/ML). You will be responsible for translating mission needs into high\-impact technical solutions, from data ingestion and model development to the deployment of full\-stack AI\-driven capabilities that directly support military operations.
Key Responsibilities:
- Provide comprehensive leadership and oversight for the data engineering, analytics, and predictive analytics team, ensuring all services align with strategic operational objectives.
- Lead the development and implementation of advanced analytics, statistical model development, data governance, and the integration of innovative technologies to inform operational decisions.
- Apply advanced machine learning and AI techniques, including unsupervised, supervised, reinforcement learning, and generative AI (e.g., Large Language Models, multi\-modal AI), to identify patterns and generate mission\-critical insights.
- Oversee the development, deployment, and maintenance of high\-quality, fault\-tolerant data pipelines to clean, tag, and ingest structured and unstructured data into data lakes and lakehouses.
- Direct the data science lifecycle, from exploration and algorithm development to statistical validation and solution operationalization, ensuring deployed capabilities meet mission requirements.
- Coordinate with the USCENTCOM Chief Data Officer (CDO), the Operations Directorate Executive Data Officer (XDO), and participate in relevant forums to interpret mission needs and define operational data requirements.
- Serve as the senior technical advisor for the team, guiding the use of query languages (SQL), programming languages (Python, R), and analytics platforms to enable data\-driven decisions.
- Translate highly technical information and findings into clear, actionable insights for a broad, non\-technical operational audience and senior leadership.
- Manage and prioritize multiple projects and tasks simultaneously in a dynamic, fast\-paced work environment.
Requirements:
- Must be a U.S. Citizen
- Active Top Secret clearance and SCI eligibility.
- BA/BS or MA/MS in Data Science, Analytics, Computer Science, Information Technology, or a related field.
- More than 10 years of relevant work experience, with significant experience leading data science or analytics teams.
- Demonstrated experience capturing, implementing, and managing data science requirements for senior Military, Federal Government, or Intelligence Community stakeholders.
- Proven expertise in applying advanced AI/ML techniques and frameworks (e.g., TensorFlow, PyTorch, Scikit\-learn) to solve complex problems.
- Highly proficient in Python and/or R for data engineering, automation, and model development.
- Experience with data engineering concepts, including building and maintaining data pipelines (ETL/ELT), data warehousing, and data lake/lakehouse architectures.
- Familiarity with data visualization and analytics platforms such as Power BI, Tableau, Qlik, or Palantir Foundry / Maven Smart System (MSS).
- Experience with data storage and processing tools (e.g., Apache Spark, Databricks, Hadoop) and databases (e.g., TSQL, MySQL, Oracle, PostgreSQL).
- Excellent written and verbal communication skills, with experience briefing senior leaders.
- Must be available for occasional travel to domestic and international locations.
Req Benefits:
Benefits \- Everforth ECS
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 Everforth ECS, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
Everforth ECS AI Hiring
Everforth ECS has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Arlington, VA, US, Macdill AFB, FL, US. Compensation range: $145K - $145K.
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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