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About This Role
About Us
Trideum Corporation is a 100% employee\-owned company, committed to embracing the world’s toughest challenges with a servant’s heart. Through dedicated hard work and commitment, we provide distinctive quality and unparalleled customer service in all aspects of our business. We also know that our employees are the key to our success, and it is our mission to take care of them so they can take care of our customers and communities where we live, work, and play.
Position Summary
The Analytics Engineer will play a pivotal role in establishing data\-driven decision\-making and supporting operational evaluations in support of the US Army Operational Evaluation Command. This position focuses on translating complex data into actional insights that enable a diverse set of stakeholders to maintain awareness, ensure quality, gain insights, and provide direction during all phases during the evaluation of US Army equipment. The Analytics Engineer will design, build, and maintain the analytical tools, dashboards, and data pipelines necessary to support all aspects of the evaluation process.
As a core member of the data and analytics team, you will be responsible for creating real\-time decision support systems that provide granular visibility into the design, planning, preparation, execution, and assessment of US Army equipment to ensure that the equipment meets the needs of warfighters. You will work closely with other data scientists, operations research analysts, and engineers to ensure that all data is traceable from source to solution. This role is essential for equipping multiple stakeholders with the tools necessary to conduct operational tests and evaluations. This position is located in Killeen, TX.
What You’ll Do
Responsibilities include, but are not limited to:
- Develop and maintain executive\- to engineering\-level decision support products, including operational dashboards and artificial intelligence (AI) pipelines.
- Build data pipelines and visualizations within enterprise platforms (such as Army Vantage) to provide real\-time oversight of operational tests and evaluations.
- Ensure strict data traceability from source data and assumptions through analyses to final evaluation.
- Work closely with customers and mission partners to define requirements and shape solutions.
- Identify data sources, engineer data pipelines, and develop data storage solutions to meet customer requirements.
- Communicate technical findings and recommendations to technical and non\-technical audiences, including senior leadership.
- Less than 25% travel may be required.
Requirements and Qualifications:
- Bachelor’s Degree from an accredited school in a technical discipline (data science, engineering, mathematics, science, computer science).
- Position requires five or more years of related professional experience.
- Active DoD Secret Clearance or U.S. citizenship and the ability to obtain/maintain a DoD Secret Clearance is required.
- Strong data science, data analytics, and/or data engineering background and thorough knowledge of statistics and mathematics.
- Expertise in data analysis and visualization using languages like Python, R, and SQL.
- Experience with data visualization software like Tableau, Power BI, or Qlik.
- Experience with data management platforms like ADVANA, Palantir Foundry, Databricks, Snowflake, and Azure Synapse Analytics.
- Demonstrated ability to solve technical and operational problems independently within a team setting.
- Excellent written and oral communication skills.
- Familiarity with DoD systems and processes.
Desired Qualifications:
- Master’s Degree from an accredited school in a technical discipline (data science, engineering, mathematics, science, computer science).
- Background in developmental or operational testing.
- Experience with data modeling, ETL, and similar data engineering processes.
- Experience with Agile processes and solutions like GitLab or Jira.
- Experience with version control software like Git.
- Experience in project management and team leadership.
- Experience using cloud platforms (e.g., Azure, AWS) for data analytics and reporting.
- Knowledge of military or defense data systems (e.g., DAVE, PRMT, GFEBS, and ARES).
We Take Care of Our People
Whether you’re looking to launch a new career or grow an existing one, Trideum is the type of company where you can balance great work with great life because we believe that taking care of our people is the right thing to do. Trideum offers:
- Competitive pay based on the work you do here and not your previous salary.
- Traditional benefits such as medical, dental, vision, life, disability, and 401k matching.
- Paid leave and the ability to cash out leave.
- Free access to certified financial planners, wellness and support services, and discount programs.
- Education assistance and professional development opportunities.
- And much more.
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Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, sexual orientation, gender identity, or any other characteristic protected by law. People with disabilities who need a reasonable accommodation to apply or compete for employment with Trideum may request such accommodation(s) by contacting Human Resources at 256\.704\.6123 or [email protected].
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 Trideum Corporation, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Trideum Corporation AI Hiring
Trideum Corporation has 2 open AI roles right now. They're hiring across Data Scientist. Positions span Killeen, TX, US, Huntsville, AL, US.
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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