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About This Role
Analytica is seeking a Data Scientist II to support a defense health mission\-critical program. The ideal candidate will be comfortable working directly with clients in a consulting and delivery capacity to develop innovative analytics, machine learning, and AI solutions that address complex business and operational challenges. This role will independently develop and deliver data science and AI solution components while owning assigned analytics, data processing, model development, visualization, and decision\-support work within multidisciplinary project teams.
The Data Scientist will work across project workstreams to analyze complex data, develop predictive and prescriptive analytic solutions, and deliver actionable insights that support data\-driven decision\-making. The candidate must be a U.S. Citizen with a SECRET clearance or the ability to obtain and maintain one. This role may be hybrid in Falls Church, Virginia, or San Antonio, Texas, or potentially remote, with periodic travel as required.
Analytica has been recognized as one of the fastest\-growing private U.S. businesses and supports government customers across health, civilian, and national security missions. Analytica offers competitive compensation, bonus opportunities, employer\-paid healthcare, professional development funding, and a 401(k) match.
Responsibilities
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- Develop and deploy analytics, machine learning, natural language processing, and AI solutions that advance mission and client objectives.
- Develop and enhance generative AI and RAG solution components using large language models, embeddings, vector databases, retrieval strategies, prompt engineering techniques, and cloud\-based AI services.
- Use AI\-assisted development tools responsibly by critically evaluating generated code and analysis, validating sources and assumptions, testing outputs, and documenting material decisions.
- Perform data acquisition, ETL, exploratory data analysis, data preparation, feature engineering, and validation using Python, SQL, and related technologies.
- Apply statistical methods, predictive modeling, machine learning, and time\-series analysis to healthcare, business, and operational challenges.
- Develop, test, evaluate, deploy, and monitor AI/ML solution components to support accuracy, reliability, scalability, security, and production performance.
- Develop dashboards, visualizations, and analytic products using Python, Power BI, Tableau, or comparable tools when required by the solution.
- Partner with clinical, operational, technical, and client stakeholders to gather requirements, translate needs into technical approaches, and communicate findings and recommendations.
- Develop and troubleshoot APIs, data services, and AI\-enabled application components while supporting integration with enterprise systems and cloud platforms.
- Collaborate through Git, code reviews, Agile ticketing, and established development practices; break work into deliverable tasks, communicate progress and dependencies, surface blockers early, and coordinate assistance when needed.
- Produce technical documentation, presentation materials, implementation guidance, and knowledge\-transfer artifacts, and contribute to technical research and continuous improvement.
Qualifications
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- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Health Informatics, Public Health, or a related field; master's degree preferred.
- Two to four years of experience developing data science, analytics, machine learning, or AI solutions in applied project environments.
- Strong proficiency in Python and SQL for data preparation, analytics, model development, testing, and deployment.
- Experience developing and evaluating machine learning models, including feature engineering, train/validation/test design, performance assessment, and error analysis.
- Working knowledge of NLP and modern AI concepts, including large language models, embeddings, RAG, prompt engineering, retrieval approaches, and output evaluation.
- Experience creating dashboards or visual analytics using Power BI, Tableau, Python, or comparable tools.
- Familiarity with API\-based solutions and integration of data, machine learning, or AI services into broader applications or enterprise environments.
- Experience collaborating in a Git\-based team environment using code reviews, Agile tickets, and software development best practices.
- Ability to work across notebook\-based, browser\-based, and local development environments, learn unfamiliar technologies quickly, manage competing priorities, and deliver assigned work independently.
- Strong communication and problem\-solving skills, including proactive status reporting, early escalation of risks and blockers, and effective communication with technical and non\-technical audiences.
- Must be a U.S. Citizen and able to obtain and maintain a SECRET security clearance.
Preferred Qualifications
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- Experience with Databricks or a comparable cloud\-based data and AI platform.
- Experience with AWS services, Amazon Bedrock, Amazon Redshift, or related cloud analytics and AI capabilities.
- Experience with generative AI applications, RAG, recommendation systems, chatbots, or emerging AI technologies.
- Experience implementing automated testing, monitoring, and quality\-assurance processes for data science or AI solutions.
- Experience supporting the Department of Defense, military or veteran healthcare, federal healthcare, or other public\-sector clients in a direct stakeholder or consulting capacity.
About Analytica: Analytica is a leading consulting and information technology solutions provider to public sector organizations supporting health, civilian, and national security missions. The company is an award\-winning SBA certified small business that has been recognized by *Inc. Magazine* each of the past three years as one of the 250 fastest\-growing companies in the U.S. Analytica specializes in providing software and systems engineering, information management, analytics \& visualization, agile project management, and management consulting services. The company is appraised by the Software Engineering Institute (SEI) at CMMI® Maturity Level 3 and is an ISO 9001:2008 certified provider.
Analytica LLC is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable federal, state, or local law. As a federal contractor, we comply with the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and take affirmative action to employ and advance in employment qualified protected veterans. We ensure that all employment decisions are based on merit, qualifications, and business needs. We prohibit discrimination and harassment of any kind. Analytica LLC also provides reasonable accommodations to applicants and employees with disabilities, in accordance with applicable law.
To enhance efficiency, fairness, and accuracy, Analytica may use AI\-assisted tools to support certain aspects of our hiring process.
- Application Review: AI tools may help identify skills and experiences relevant to the role.
- Interview Support: AI\-powered notetaking tools may be used during interviews to document discussions and summarize key points.
These tools are used to assist our team. All hiring decisions are made by Analytica recruiters and hiring managers.
By submitting an application, you acknowledge that AI\-assisted tools may be used to support parts of the application and interview process.
When receiving email communication from Analytica, please ensure that the email domain is analytica.net to verify its authenticity.
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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 Analytica, 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.
Analytica AI Hiring
Analytica has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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