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About This Role
Advanced Trade Analytics Platform (ATAP)
Remote (U.S.)
Active Top Secret Clearance Required
Solve Complex Problems with Data, AI, and Advanced Analytics
Radiance Technologies is seeking a Data Scientist to support U.S. Customs and Border Protection's (CBP) Advanced Trade Analytics Platform (ATAP). In this role, you will develop advanced analytical solutions, machine learning models, and data\-driven insights that support critical trade enforcement and national security missions.
Working alongside government leaders, analysts, engineers, and fellow data scientists, you will transform complex datasets into actionable intelligence that improves operational effectiveness and supports informed decision\-making.
What You'll Do
- Apply statistical analysis, machine learning, and artificial intelligence techniques to solve complex business and mission challenges.
- Analyze large datasets to identify trends, patterns, anomalies, and operational insights.
- Develop predictive, prescriptive, and advanced analytical solutions.
- Design, build, evaluate, and refine machine learning models.
- Develop data visualizations, dashboards, and analytical products that communicate findings effectively.
- Transform raw data into actionable recommendations for stakeholders.
- Collaborate with customers to understand mission needs and operational requirements.
- Support enterprise analytics initiatives and decision\-support capabilities.
- Participate in model validation, testing, and quality assurance activities.
- Provide technical leadership and mentoring within project teams.
- Participate in Agile development activities and cross\-functional collaboration.
Required Qualifications
Education
Master's Degree or Ph.D. in:
- Data Science
- Statistics
- Applied Mathematics
- Computer Science
- Operations Research
- Artificial Intelligence
- Or a related quantitative discipline
*A Ph.D. may substitute for up to three years of relevant experience.*
Experience
- Six (6\) years of experience in applied research, big data analytics, statistics, data science, computer science, operations research, or related quantitative disciplines.
- Three (3\) years of direct machine learning experience.
- Experience working with large\-scale structured and complex datasets.
- Experience developing predictive and prescriptive analytics solutions.
- Experience designing and implementing statistical and machine learning models.
- Experience supporting enterprise analytics and decision\-support initiatives.
Knowledge \& Skills
- Expertise in data mining, statistical analysis, machine learning, artificial intelligence, and data visualization.
- Strong analytical, quantitative, and problem\-solving skills.
- Ability to communicate analytical concepts and findings to technical and non\-technical audiences.
- Ability to independently lead analytical efforts from concept through implementation.
Preferred Qualifications
- Experience supporting CBP, DHS, defense, intelligence, law enforcement, financial crimes, or trade\-related programs.
- Experience with trade analytics, fraud detection, risk scoring, compliance monitoring, or investigative analytics.
- Experience developing production\-ready machine learning solutions.
- Familiarity with Natural Language Processing (NLP), Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), Explainable AI, and MLOps practices.
- Experience with cloud\-based analytics and enterprise data platforms.
Preferred Technical Skills
- Python
- SQL
- R
- Spark
- Databricks
- TensorFlow
- PyTorch
- Scikit\-Learn
- Power BI
- Tableau
- Git
- Agile Methodologies
Security Requirements
- Active Top Secret security clearance required.
- Ability to obtain and maintain access to DHS and CBP systems and facilities.
Why Radiance?
Radiance Technologies is a 100% employee\-owned company dedicated to solving complex national security challenges through innovation, technical excellence, and mission\-focused solutions. We provide opportunities to work on meaningful programs, collaborate with talented professionals, and grow your career while making a direct impact.
Benefits
- 100% Employee\-Owned (ESOP)
- Remote Work Flexibility
- Meaningful National Security Mission
- Exposure to Advanced Analytics, AI, and Big Data Technologies
- Career Growth and Professional Development
- Collaborative and Innovative Culture
- Competitive Compensation and Benefits
Join Radiance Technologies and help transform data into mission success.
Radiance Technologies is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
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 Radiance Technologies 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Radiance Technologies Inc. AI Hiring
Radiance Technologies Inc. has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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