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About This Role
Job Type: Full time
Workplace Type: Onsite in Stuttgart, Germany
Clearance: Active TS/SCI with eligibility to obtain a CI Polygraph
Must be a U.S. Citizen
Benefits: Medical, dental, and vision coverage, 401k matching, generous PTO, paid holidays, professional training opportunities, and even pet insurance to ensure your furry friends are cared for too.
Job Summary
Castalia Systems is currently searching for a Senior Data Scientist who will leverage advanced data science, Python, AI/ML, statistical analysis, and ABI tools and tradecraft to analyze, integrate, and visualize complex mission data. This role will develop dashboards, perform data quality and preprocessing activities, and apply machine learning techniques to support actionable intelligence and mission\-focused decision\-making.
Roles and Responsibilities
A qualified candidate will perform the following duties and responsibilities, but are not limited to:
- Develop and maintain Python\-based data processing and analytical workflows to support mission\-driven geospatial and intelligence analysis.
- Apply statistical analysis, hypothesis testing, and machine learning techniques to identify patterns, trends, anomalies, and relationships within complex datasets.
- Design and implement automated methodologies to improve the efficiency, repeatability, and scalability of data analysis processes.
- Integrate and analyze structured and unstructured data from multiple sources to support intelligence production and decision\-making.
- Develop and refine AI/ML models used for classification, pattern recognition, predictive analysis, and other advanced analytical use cases.
- Create interactive dashboards, visualizations, and analytical products using Tableau, Power BI, or similar tools to communicate findings to mission stakeholders.
- Conduct data validation, quality assurance, and performance assessments to ensure analytical outputs are accurate, reliable, and mission relevant.
- Utilize Activity\-Based Intelligence (ABI) tools and tradecraft to identify significant activities, relationships, behaviors, and patterns across datasets.
- Collaborate with analysts, data engineers, and mission stakeholders to translate intelligence requirements into data science methodologies and analytical solutions.
- Document data science methodologies, model performance, analytical assumptions, and results to ensure reproducibility and effective communication of findings.
Required Qualifications:
- Bachelor’s degree in an area related to the labor category (i.e., Data Science/Engineering, IT, Computer Science, Information Science) from a college or university accredited by an agency recognized by the U.S. Department of Education or 7 years of experience conducting data science support, with at least a portion of the experience within the last 2 years
- 11 years of specific experience without a degree may be considered
- Produce data cleaning, basic analysis, and preparation for visualization
- Develop dashboards and perform routine data quality checks
- Perform data preprocessing, data integration, quality checks and assurance, and complex data visualization creation
- Develop dashboards with tools like Tableau or Power BI, and apply AI/ML models for unstructured data analysis
Preferred Qualifications:
- Intelligence Community experience preferred
Requirements/Work Environment
- Normal office environment. No shift work.
Travel
- Less than 10% travel
Company Description
Castalia Systems is a proven business partner providing mission critical solutions to the Federal Government. We provide cutting edge solutions from Securing and Managing Data to Systems Engineering and Development. Castalia Systems is a pioneer in Artificial Intelligence Design and Application.
With our vast knowledge of our customers’ needs and relevant technology, our team is able to bring successful solutions to every mission. We are one\-upping our competitors by providing premium IT solutions and platforms with cutting\-edge technology so it’s so evident when you compare us with anyone.
Compensation
At Castalia Systems, we provide you with opportunities and choices and support your total well\-being. Our benefits include: Medical, dental, vision coverage, 401k matching, generous PTO, paid holidays, professional training opportunities, and even pet insurance to ensure your furry friends are cared for too. All regularly scheduled employees working at least 30 hours per week are eligible to participate in Castalia Systems’ benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits.
Salary at Castalia Systems is determined by various factors, including but not limited to location, position knowledge, skills, competencies, and experience, as well as contract\-specific affordability and organizational requirements. In addition to competitive base compensation, we offer a comprehensive overseas compensation package designed to make your transition to Stuttgart as seamless as possible. Eligible employees may receive Cost of Living Allowance (COLA), relocation assistance, and per diem, with support available for both individuals and families relocating to Germany.
Disclaimer
Castalia Systems is an equal employment opportunity and affirmative action employer and strives to comply with all applicable laws prohibiting discrimination based on race, color, creed, sex, sexual orientation, age, national origin, or ancestry, physical or mental disability, veteran status, marital status, HIV\-positive status, as well as any other category protected by federal, state, or local laws. All such discrimination is unlawful, and all persons involved in the operations of the company are prohibited from engaging in this type of conduct.
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 Castalia Systems, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Castalia Systems AI Hiring
Castalia Systems has 1 open AI role right now. They're hiring across Data Scientist. Based in Arlington, VA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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