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About This Role
Description:
Who We Are
Aptima is a technological leader in the national security industry. Our mission is to drive the future of national security by engineering scalable solutions that fuse technological innovation with human potential to transform how individuals and teams train, develop, and perform in mission\-critical environments.
Our culture is rooted in our core values, which have evolved over time and our employees have embraced: Integrity, Ingenuity, Excellence, Respect, Engagement, Teamwork. At our core, Aptima researches, develops, and innovates within an area that engineering firms largely ignore, the human component. To impact the world in meaningful ways, you must bring those innovations to light, and that is precisely what we do.
How You Will Make An Impact
The Senior Data Scientist leads data driven problem solving, including design and technical execution, by applying modern AI/ML techniques in dynamic systems that drive our tech enabled solutions for our Department of War (DoW) customers. This role combines expertise in translating problem requirements and model selection with applied AI system design and project leadership, serving as a technical lead on multidisciplinary programs and coordinating the work of AI engineers and researchers. The position emphasizes end\-to\-end data modeling ownership, hands on interaction with stakeholders, and translation of mission needs into operationally effective capabilities, while contributing to proposals, customer engagements, and internal technical excellence.
Key Responsibilities
- Lead the design of ML/AI models, interaction workflows, and recommendation system’s designed for skill selection or staff profiles.
- Understand the science that can inform requirements addressing trust, explainability, complexity, and characteristics of modern ML solutions.
- Serve as technical lead or task lead on AI and human–AI system development efforts, coordinating work across AI engineers and researchers.
- Translate mission needs and human performance objectives into system requirements, technical architectures, and evaluation plans. Review and guide system implementations to ensure usability, explainability, and operational suitability.
- Design and execute experiments and statistical studies to evaluate traditional AI/ML, as well as generative AI and agentic systems.
- Design, develop, and oversee analytic pipelines and performance metrics to operationalize complex behavioral and organizational constructs using real\-world data.
- Translate quantitative results into clear, actionable insights for technical teams, behavioral scientists, and government stakeholders, bridging theory, data, and system implementation.
- Lead technical contributions to proposals, system designs, and customer deliverables. Present technical work internally and at relevant external forums to include conferences, customer briefings, and other venues.
- Mentor junior staff and promote best practices in human\-centered AI system development. Contribute to internal initiatives for AI system development and evaluation.
Requirements:
- MS or PhD in data science, computer science, applied mathematics, physics, or related STEM field.
- Demonstrated expertise in the AI/ML applications and experiments, spanning stakeholder engagement, requirements scoping, and development of technical solutions.
- Strong quantitative modeling and statistical analysis background, including experimental design, supervised and unsupervised learning, and multimodal analysis.
- Hands\-on experience with AI agent development and evaluation, including agentic workflow design, tool integration, and output benchmarking using frameworks such as LangChain and LangGraph.
- Proficiency in Python for data analysis, modeling, simulation, visualization, and development of reproducible analytic workflows; experienced with tools such as pandas, NumPy, Matplotlib/Plotly, and Jupyter\-based environments.
- Proficiency in SQL for data querying, transformation, and pipeline development across relational and cloud\-based data platforms.
- Experience developing custom metrics, indices, and analytic pipelines to operationalize complex behavioral, organizational, or team constructs using real\-world, imperfect data.
- Experience designing and executing human\-subject experiments and analyzing performance data.
- Experience leading technical tasks or serving as technical lead or PI on government\-funded programs.
- Strong written and verbal communication skills, including proposal and customer\-facing technical work.
- Proven ability to translate quantitative results into clear, actionable insights for technical teams, behavioral scientists, and non\-technical stakeholders, serving as a bridge between disciplines.
- Ability to manage and coordinate multidisciplinary technical teams.
- Ability to obtain and maintain a security clearance; familiarity with DoW research environments preferred.
- Willingness to travel as needed.
*All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or any other status protected by applicable national, federal, state or local law.*
Salary Context
This $135K-$150K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Aptima 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 28% below the category median. Disclosed range: $135K to $150K.
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.
Aptima Inc AI Hiring
Aptima Inc has 1 open AI role right now. They're hiring across Data Scientist. Based in Fairborn, OH, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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