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About This Role
Job ID
329092
Job Title: Data Scientist \- AI/mL Specialist
Job Category: Science
Time Type: Full time
Minimum Clearance Required to Start: Public Trust
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Continental US
\* \* \*The Opportunity:
CACI is seeking a Senior Data Scientist to support the Department of Homeland Security's Cyber Crimes Center (C3\) in developing advanced AI and machine learning solutions for critical law enforcement investigations. In this role, you will:
- Design and deploy production\-grade AI/ML systems that enable investigators to combat cybercrime and dismantle criminal networks
- Work within a collaborative technical team to deliver AI solutions that meet rigorous federal compliance standards
- Contribute to mission\-critical national security operations while advancing responsible AI practices
- Apply cutting\-edge machine learning techniques to solve complex investigative challenges
This position requires U.S. citizenship and eligibility to obtain a DHS Public Trust clearance.
Responsibilities:
- Design and implement production AI/ML systems compliant with federal, DHS, and ICE governance requirements
- Develop explainable and auditable AI solutions with integrated human\-in\-the\-loop oversight mechanisms
- Build machine learning models for investigative analytics including entity resolution, network analysis, and anomaly detection
- Develop scalable data pipelines and ETL processes using Apache Airflow, Spark, and on\-premise infrastructure
- Design database solutions optimized for AI training, fine\-tuning, and real\-time inference on investigative data Implement continuous monitoring systems to detect bias, performance degradation, and security vulnerabilities
- Support AI Use Case approval processes, Security Authorization (ATO), and risk management protocols
- Document system architecture, data flows, and model explanations for audit and transparency requirements
- Integrate AI capabilities with existing investigative systems through APIs and microservices
- Ensure data provenance documentation and lawful data acquisition per federal requirements
- Collaborate with software engineers, infrastructure architects, and cybersecurity specialists on cross\-functional projects
- Communicate technical concepts and model decisions to both technical and non\-technical stakeholders
- Maintain compliance with AI Security Control Baseline and federal governance frameworks
Contribute technical expertise to team initiatives and share knowledge and best practices with team members
*
Qualifications:
*Required:*
- Bachelor's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or related technical field
- 5\+ years of demonstrated AI/ML experience with a proven track record of delivering production AI systems
- Advanced proficiency with machine learning frameworks (TensorFlow, PyTorch, scikit\-learn) and deep learning architectures
- Expert\-level Python programming with extensive experience in Pandas, NumPy, and Spark
- Demonstrated experience deploying and maintaining production AI systems at scale
- Experience with on\-premise infrastructure, data centers, and physical server environments
- Deep knowledge of explainable AI techniques and model interpretability methods (SHAP, LIME)
- Proven experience developing AI systems that comply with federal governance and ethical standards
- Proficiency with MLOps practices, model versioning, CI/CD pipelines, and workflow orchestration
- Strong communication skills with ability to convey complex technical concepts to diverse audiences
Active Public Trust clearance or ability to obtain and maintain clearance
*
*Desired:*
- Master's degree or PhD in a related technical field
- Experience with Generative AI, large language models, prompt engineering, and fine\-tuning techniques
- Knowledge of AI security, bias detection and mitigation, and adversarial machine learning
- Background in federal law enforcement or intelligence community AI applications
- Expertise in Open\-Source Intelligence (OSINT) analysis and cyber threat intelligence
- Proficiency with Elasticsearch, Kibana, graph databases, and investigative analytics platforms
- Familiarity with federal AI policies including Executive Orders 13960/14110, OMB guidance, and DHS/ICE directives
- Experience with real\-time streaming analytics and Change Data Capture (CDC)
This position is contingent on funding and may not be filled immediately. However, this position is representative of positions within CACI that are consistently available. Individuals who apply may also be considered for other positions at CACI.What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you’ll be part of a high\-performing group dedicated to our customer’s missions and driven by a higher purpose – to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers \- every day. You’ll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground — in your career and in our legacy.
Pay Range:
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$94,400 \- $198,200*CACI is* *an Equal Opportunity Employer.* *All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any* *other protected characteristic.*
Salary Context
This $94K-$198K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CACI International, 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. This role's midpoint ($146K) sits 24% below the category median. Disclosed range: $94K to $198K.
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.
CACI International AI Hiring
CACI International has 6 open AI roles right now. They're hiring across Data Scientist, Research Engineer, AI/ML Engineer, Prompt Engineer. Positions span Camp Smith, HI, US, Florham Park, NJ, US, Remote, US. Compensation range: $133K - $290K.
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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