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About This Role
Location: Remote
Job Type: Full\-Time
Clearance Requirement: Secret Clearance (Top Secret Preferred)
U.S. Citizenship is Required.
Position Overview:
Praescient Analytics is seeking a highly skilled and visionary Senior Data Scientist to provide enterprise\-level analytical leadership, advanced statistical modeling, and machine learning oversight for the National Background Investigation Services (NBIS) and Enterprise Information Technology (EIT) application development suites. In this role, you will define the overarching predictive analytics vision, establish rigorous machine learning workflows, and conceptualize advanced statistical models optimized for a secure cloud environment. Working without supervision on highly complex initiatives, you will lead the evolution of traditional reporting into modern, highly scalable, and secure predictive models within an AWS GovCloud environment. You will collaborate closely with principal data architects, data engineers, chief data officers, and federal program stakeholders, exercising wide latitude for independent judgment to ensure our nation's background investigation analytics frameworks are resilient, secure, and compliant with zero\-trust mandates. Key Responsibilities: Advanced Statistical Analysis \& Modeling: Formulate, experiment with, and articulate alternative statistical frameworks. Apply rigorous experimental design, hypothesis testing, and statistical analysis to massive federal datasets, validating results against strict requirements and security assumptions.
Machine Learning Engineering \& Vision: Design, build, and deploy end\-to\-end machine learning pipelines (including predictive modeling, anomaly detection, and NLP), aligning model development life cycles with the Agile SAFe v6\.0 roadmap.
Scalable Analytics \& Cloud Optimization: Author the scripts and frameworks for modernizing legacy analytics systems into highly distributed, cloud\-native processing and intelligence solutions specifically optimized for AWS GovCloud.
Data Exploration \& Advanced Querying: Establish standardized methodologies for data extraction, manipulation, and feature engineering across enterprise\-grade relational and non\-relational datastores. Maintain strict data integrity and reproducible research practices.
High\-Throughput Integration Strategy: Partner with data engineers to design and integrate ML models directly into high\-volume, low\-latency batch and stream processing pipelines (e.g., Kafka, Spark) that effortlessly bridge application and analytics tiers.
Cross\-Functional Collaboration: Serve as the principal data science advisor across the entire program. Work with software engineers, data architects, and customer application experts to evaluate technical trade\-offs and drive advanced analytics capabilities.
Agile Requirements Optimization: Leverage Jira, Confluence, and SAFe processes to translate high\-level mission capabilities and agency mandates into foundational analytical epics, technical features, and modeling roadmaps.
Required Qualifications:
Clearance \& Citizenship: Active U.S. Secret clearance is required. Must be a U.S. Citizen to meet federal contract mandates.
Education \& Experience: \* Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field with 10–12 years of relevant data science and machine learning experience OR Master’s degree in a related field with 8–10 years of relevant experience OR PhD in a related field with 5–7 years of relevant experience.
Mastery of Statistics \& Machine Learning: Deep, extensive proficiency in advanced statistical methods (regression analysis, forecasting, causal inference) and machine learning algorithms (supervised/unsupervised learning, ensemble methods, deep learning, NLP).
Expert\-Level Programming \& Querying: Mastery of Python (including PyData stack: Pandas, NumPy, Scikit\-Learn, SciPy) and advanced SQL (complex joins, window functions, analytical queries) for handling multi\-million row datasets.
AWS GovCloud Ecosystem: Comprehensive experience designing and deploying scalable ML models and analytics pipelines within AWS and AWS GovCloud, specifically utilizing SageMaker, Redshift, Glue, EMR, Athena, S3, and DynamoDB.
Advanced Database Experience: Expert\-level knowledge of extracting and manipulating data from enterprise\-grade relational databases (Oracle, PostgreSQL), NoSQL engines, and massive parallel processing (MPP) data warehouses.
Tools \& Lifecycle Management: Expert proficiency with Git, Jira, Confluence, and MLOps principles within an automated DevSecOps environment to manage model versioning and deployment pipelines.
Autonomy \& Communication: Proven ability to work without supervision on highly complex, mission\-critical projects, exercising wide latitude for independent judgment. Strong interpersonal skills with the ability to articulate complex mathematical and analytical visions to both engineering teams and senior federal stakeholders.
Preferred Qualifications:
An active Top Secret (TS) clearance.
Prior experience providing enterprise data science oversight or technical leadership directly on Department of Defense (DoD), DCSA, or NBIS programs.
Advanced expertise in federal data compliance frameworks (e.g., NIST, HIPAA), responsible AI principles, and automated data encryption at rest and in transit within machine learning workflows.
The projected compensation range for this position is $135,000\.00 to $165,000\.00 (annualized USD).
What You Can Expect From Us:
- Real opportunity for career growth in an environment where your achievements will be celebrated
- Constant collaboration with numerous teams to ensure client success
- A team that respects and embraces your ideas and expertise
- Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain
- A workplace dedicated to supporting and bettering public safety and government agencies
Benefits:
- Very competitive salary based on qualifications and experience
- Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
- 401(k) with company match
- Travel \& performance incentives
- 3 weeks paid time off (plus Federal Holidays)
- $5K annual training allowance
Praescient Analytics is a Certified Woman\-Owned Small Business (WOSB) with over a decade of expertise in advanced analytics, engineering, and DevOps, specializing in transforming complex data into actionable intelligence for informed decision\-making. Since 2011, we have supported over 40 organizations across diverse domains, including military intelligence operations, financial and fraud investigations, and insider threat detection.
Our team of experts—skilled in cloud computing, artificial intelligence, machine learning, data science, DevOps, and engineering—brings deep experience in solving complex challenges. With a proven track record in federal contracting, we deliver tailored, high\-impact solutions designed to enhance operational efficiency, ensure mission success, and address the evolving needs of our clients. Praescient's innovative and adaptive approach makes us a trusted partner in delivering data\-driven insights and technological excellence for critical missions.
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
US Citizenship Required
Interested Candidates: Please forward your resume to recruiting@praescientanalytics.com and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.
Salary Context
This $135K-$165K 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 Praescient Analytics, 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 ($150K) sits 22% below the category median. Disclosed range: $135K to $165K.
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.
Praescient Analytics AI Hiring
Praescient Analytics has 1 open AI role right now. They're hiring across Data Scientist. Based in Fairfax, VA, US. Compensation range: $165K - $165K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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