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About This Role
Job Description:
Prescient Edge is seeking a Senior Data Scientist to support a federal government client.
Please note that the availability of this position is contingent upon.
Benefits \& Compensation:
At Prescient Edge, we believe that acting with integrity and serving our employees is the key to everyone's success. To that end, we provide employees with a best\-in\-class benefits package that includes:
- A competitive salary with performance bonus opportunities.
- Comprehensive healthcare benefits, including medical, vision, dental, and orthodontia coverage.
- A substantial retirement plan with no vesting schedule. Career development opportunities, including on\-the\-job training, tuition reimbursement, and networking.
- A positive work environment where employees are respected, supported, and engaged.
- Salary range: $135,000 \- $210,000\. Salary to be determined by the education, experience, knowledge, skills and the abilities of the applicant internal equity, and alignment with market data.
Description:
- Conduct data science functions on structured and unstructured data to streamline intelligence analysis and production, enrich results, and develop and maintain Python code.
- Author cogent and logical scripts using Python and other applicable languages in a virtual environment using common Integrated Development Environments (IDE) such as VS Code, Spyder, PyScript, or Jupyter Notebooks.
- Design and develop methods, processes, and systems to consolidate and analyze structured and unstructured data from diverse sources including "big data" sources using Python; demonstrate expert knowledge of Pandas and geospatial functions within Python.
- Develop and use advanced software programs, algorithms, query
- Perform research on various data sources, including structured and unstructured data using quantitative and qualitative metadata and content analytics.
- Construct and perform complex database queries in multiple SIGINT databases and Application Programming Interface (API) interfaces utilizing Python and other applicable computer languages.
- Techniques, models to solve complex intelligence problems, and automated processes to normalize, integrate, and evaluate data.
- Debug existing and future Python code; refactor legacy code to ensure continued security, functionality, and compatibility.
- Document and block\-comment all code to ensure recoverability and error\-checking, and enhance reading, checking, and maintaining code in accordance with common data science and coding standards, such as PEP\-8 for Python,5 or using style\-guide features embedded in common IDE applications, such as Spyder, VS Code, PyScript or others upon approval by the Government.
- Demonstrate knowledge of NSA data architecture, Application Programming Interfaces (APIs), and analyst tools.
- Collaborate across multi\-discipline teams to ensure connectivity between various data sources and business problems.
- Identify meaningful insights, interpret, and communicate findings, plus make recommendations to stakeholders.
- Analyze requirements and evaluate technologies for data science capabilities including Natural Language Processing, Machine Learning, predictive modeling, statistical analysis, and hypothesis testing.
Maintain awareness of emerging analytics and big\-data technologies.
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Job Requirements: Experience:
- Over 10 years of relevant experience.
- Advanced knowledge of Python and other relevant languages; relevant STEM degree and/or cyber certifications.
- Demonstrate experience and knowledge of computer science concepts, data architecture, intelligence practices, and managing data in a big\-data environment.
- Demonstrate expert knowledge of Python, and Jupyter Notebooks and/or Jupyter Labs.
Education:
- Associate’s degree or higher in a cyber or relevant STEM discipline.
- Complete required course NSA NETA1400 (Technology Fundamentals for Analysis); recommended completion of any of the following NSA Courses: NETA2402/NETA2108 (Analysis II), RPTG2238, RPTG2235, RPTG3225, RPTG3222 (Basic Analytical Reporting) or equivalent curriculum.
- Complete recommended certifications: Data Science Council of America (DASCA) certifications, such as Associate Big Data Engineer (ABDE), Associate Big Data Analyst (ABDA), and Senior Data Scientist (SDS); Google Data Analytics Professional Certificate; IBM Data Science Professional Certification.
Security Clearance:
- Security clearance required TS/SCI w/ CI Polygraph
Location:
- Colorado Springs, CO.
Salary Context
This $135K-$210K range is above 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 Prescient Edge, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($172K) sits 11% below the category median. Disclosed range: $135K to $210K.
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.
Prescient Edge AI Hiring
Prescient Edge has 1 open AI role right now. They're hiring across Data Scientist. Based in Colorado Springs, CO, US. Compensation range: $210K - $210K.
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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