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About This Role
Data Scientist
Wyetech is seeking an experienced Data Scientist level 3 to support a mission-driven initiative focused on modernizing and automating system security evaluation and authorization processes within a mission-critical Intelligence Community environment.
The selected candidate will help transform traditionally manual security evaluation processes into an automated, efficient, and intelligence-driven model. This position plays a critical role in enabling continuous monitoring, accelerating decision-making, and strengthening security throughout the lifecycle of critical systems and projects.
The ideal candidate possesses extensive experience in data analytics, statistical analysis, programming, and data engineering, with the ability to develop automated solutions that improve security assessments, identify anomalies, and support the Risk Management Framework (RMF).
This opportunity supports a highly visible enterprise application with nearly 3,000 unique active monthly users, providing opportunities to collaborate with technical teams and organizational leadership on new capabilities, system improvements, and mission-critical initiatives.
Key Responsibilities
Data Science & Advanced Analytics
- Develop, maintain, and enhance analytical capabilities that automate security evaluation and authorization processes.
- Analyze complex datasets to identify trends, patterns, anomalies, and potential security concerns.
- Use Python and other analytical programming languages to process, analyze, and validate security-related data.
- Develop analytical methodologies to evaluate whether systems and resources meet established security requirements.
- Detect anomalies and outliers to assess system progress through the Risk Management Framework (RMF) lifecycle.
- Identify relationships and associations between system resources based on attributes, configurations, and known system information.
- Apply statistical analysis and data science techniques to support automated assessments and operational decision-making.
- Develop scalable analytical solutions that improve the accuracy, efficiency, and reliability of security evaluations.
- Collaborate with technical teams to translate complex data into actionable insights.
Data Engineering & ETL Development
- Design, develop, implement, and maintain Extract, Transform, Load (ETL) pipelines.
- Collect and integrate data from multiple sources, including network sensors, security tools, and compliance databases.
- Transform, normalize, and prepare data for reporting, analytics, and automated security assessments.
- Develop scalable data processing workflows supporting continuous monitoring and security automation.
- Perform data validation and quality assurance checks to ensure data accuracy, completeness, and consistency.
- Monitor data pipelines, troubleshoot ingestion issues, and optimize processing workflows.
- Support timely and reliable delivery of security-related information to stakeholders and enterprise applications.
- Identify opportunities to improve data integration, pipeline efficiency, and analytical performance.
- Collaborate with engineering teams to ensure dataflows support mission requirements and system functionality.
Security Analytics & RMF Automation
- Develop automated capabilities that replace manual security evaluation and authorization activities.
- Support continuous monitoring and assessment throughout the RMF lifecycle.
- Analyze security-related datasets to identify compliance gaps, anomalies, and potential operational risks.
- Develop analytical methods to assess security requirements and system authorization readiness.
- Identify resource associations and relationships to improve visibility into enterprise systems.
- Support automation of security assessments, data validation, and reporting processes.
- Develop data-driven solutions that enable more efficient security reviews and decision-making.
- Collaborate with cybersecurity professionals, engineers, and Government stakeholders to improve security evaluation workflows.
- Support enterprise-wide security modernization and automation initiatives.
Statistical Modeling & Predictive Analytics
- Apply statistical analysis techniques to evaluate complex datasets and identify meaningful trends.
- Support the development, testing, and refinement of predictive analytical models.
- Identify anomalies, outliers, correlations, and patterns within security and operational data.
- Evaluate analytical model performance, accuracy, and reliability.
- Apply regression analysis, machine learning, and other statistical methodologies where appropriate.
- Develop analytical approaches that support forecasting, risk identification, and operational insights.
- Translate analytical findings into clear, actionable recommendations.
- Identify opportunities to improve automated decision-making through advanced analytics.
Data Visualization & Reporting
- Develop and maintain analytical reports, visualizations, dashboards, and performance metrics.
- Translate complex datasets into understandable visual representations for technical and nontechnical stakeholders.
- Support reporting capabilities that provide visibility into security posture, RMF progress, and operational performance.
- Identify trends and performance indicators to support continuous improvement initiatives.
- Collaborate with stakeholders to define reporting requirements and analytical objectives.
- Support the development of metrics and dashboards that improve enterprise-level situational awareness.
- Communicate analytical findings, data quality concerns, and recommended improvements to technical teams and leadership.
Agile Development & Technical Collaboration
- Participate in Agile development activities within a two-week sprint cadence and 90-day Program Increment.
- Collaborate with software engineers, data engineers, cybersecurity professionals, and other technical stakeholders.
- Support sprint planning, technical reviews, and project coordination activities.
- Identify and resolve data processing, analytical, and integration challenges.
- Document analytical methodologies, data processing workflows, and technical solutions.
- Support troubleshooting, testing, validation, and continuous improvement of analytical capabilities.
- Contribute to the development of new features and enterprise system enhancements.
- Assist with identifying, reporting, and resolving production issues affecting data reliability or system functionality.
Required Qualifications
- Active TS/SCI security clearance with current Full Scope Polygraph (FSP) is required.
- Ten (10) years of experience analyzing datasets and developing analytics, subject to the PhD experience substitution described below.
- Ten (10) years of programming experience using R, Python, SAS, or MATLAB, subject to the PhD experience substitution described below.
- Demonstrated experience analyzing complex datasets and developing analytical solutions.
- Experience using analytical programming languages to process, transform, validate, and analyze data.
- Experience supporting data-driven decision-making through analytical development and evaluation.
- Strong analytical, mathematical, and problem-solving abilities.
- Ability to develop and maintain technical documentation supporting analytical methodologies and data processing workflows.
- Excellent written and verbal communication skills.
- Ability to collaborate effectively with cross-functional technical teams and stakeholders.
Education & Experience Requirements
Candidates must meet the applicable education and experience requirements:
Option 1: Bachelor's Degree Without a Master's Degree
- Bachelor's degree in a quantitative discipline, such as:
+ Statistics + Mathematics + Operations Research + Engineering + Computer Science
- Twelve (12) total years of relevant experience, consisting of the ten-year experience requirement plus two additional years in software development, cloud development, data analysis, or analytics development.
Option 2: Bachelor's Degree and Master's Degree (or Higher)
- Bachelor's degree and a master's degree or higher in a quantitative discipline, such as Statistics, Mathematics, Operations Research, Engineering, or Computer Science.
- Ten (10) years of qualifying experience, with no additional two-year experience requirement.
Option 3: PhD Experience Substitution
- A PhD in a quantitative discipline may substitute for four (4) years of the experience requirement, reducing the applicable ten-year requirement to six years.
- Experience and education must otherwise satisfy the applicable position qualifications.
Preferred Qualifications
- Strong background in statistical analysis and advanced analytical methodologies.
- Experience developing, tuning, validating, and testing predictive models.
- Experience creating analytical dashboards, data visualizations, charts, and reports.
- Experience with Elasticsearch for data indexing, querying, analysis, or integration.
- Experience using Regular Expressions (RegEx) for pattern matching and data processing.
- Experience applying machine learning techniques to complex datasets.
- Familiarity with Natural Language Processing (NLP) methodologies and applications.
- Experience with regression analysis and predictive analytics.
- Strong programming experience using Python.
- Experience developing analytical solutions using MATLAB or R.
- Experience working with SQL or MongoDB databases.
- Experience with metrics and monitoring databases and visualization technologies, including:
+ Grafana + Graphite + InfluxDB
- Experience designing, developing, and optimizing ETL pipelines.
- Experience integrating data from security tools, network sensors, and compliance systems.
- Experience supporting data quality validation, pipeline monitoring, and workflow optimization.
- Familiarity with cybersecurity analytics, continuous monitoring, and RMF processes.
- Experience supporting automated security assessments and enterprise-level data analytics.
- Experience working within Agile software development environments.
Work Schedule & Flexibility
This position offers a flexible work schedule while maintaining established collaboration hours and supporting mission requirements.
Core Working Hours
- Core collaboration hours are 10:00 AM – 2:00 PM.
- Employees have flexibility to establish their schedules outside core hours, subject to operational requirements.
- Team members currently begin their workdays as early as 5:30 AM, with some working until approximately 8:00 PM.
- Employees are encouraged to maintain consistent schedules to support collaboration and team coordination.
- Scheduled operations conclude at 9:00 PM and are not conducted on weekends or holidays.
- Work schedules are subject to applicable Code Yellow/Red weather or emergency operating conditions.
- Major team meetings are scheduled between 10:00 AM and 2:00 PM.
Work-From-Home Opportunity
- Eligible employees may be considered for one work-from-home day per week after completing 60 days of employment.
- Work-from-home opportunities are generally available on Wednesday or Thursday, depending on prioritized work that can be performed productively remotely.
- Individuals must be submitted for approval before participating in a work-from-home arrangement.
- Remote work is subject to Government approval, mission requirements, and operational priorities.
Agile Sprint Cadence
- The team operates on two-week sprint cycles.
- Program Increment planning occurs on a 90-day cadence.
- Full team attendance is required during Program Increment planning activities.
- Major team meetings and collaborative activities are scheduled during established core hours.
Project Visibility & Impact
This position supports a highly visible enterprise-level project with nearly 3,000 unique active monthly users, and the user base continues to grow.
The project receives significant attention from organizational leadership, providing team members with opportunities to contribute to discussions involving new features, technical improvements, and future capabilities.
The team is responsible for supporting reliable production operations, identifying and reporting issues, and contributing to solutions that maintain system performance and availability.
The selected candidate will have the opportunity to:
- Support a mission-critical enterprise application with a growing user base.
- Develop advanced analytical capabilities that improve cybersecurity evaluation and authorization processes.
- Contribute to enterprise-wide automation and modernization initiatives.
- Collaborate with engineering teams, cybersecurity professionals, and Government stakeholders.
- Participate in discussions surrounding new features and capabilities requested by organizational leadership.
- Help improve data reliability, security monitoring, and analytical decision-making.
- Support a collaborative technical environment with flexible scheduling opportunities.
- Make a direct impact on the effectiveness and efficiency of mission-critical security operations.
Clearance Requirements
Active TS/SCI security clearance with current Full Scope Polygraph (FSP) is required.
Due to federal contract requirements, United States Citizenship and position-appropriate security clearance are required.
The Wyetech Advantage
At Wyetech, you’ll be at the center of an award-winning corporate culture, breaking technological barriers and solving real-world problems for our federal government customers. We are committed to hiring the best of the best, and in return, we offer a world-class, truly unique employee experience that is rare within our industry.
Wyetech believes in generously supporting employees as they prepare for retirement. The company automatically contributes 20% of each employee's gross compensation to a Simplified Employee Pension (SEP) IRA, with no requirement for employee matching. All contributions are fully vested from day one, ensuring immediate ownership of retirement funds.
Additional benefits include:
- Wyetech provides a generous PTO plan of up to 200 hours annually, aligned with applicable state leave regulations. Employees have the flexibility to adjust their PTO allocation at the start of each calendar year, ensuring it meets their evolving needs.
Full-time employees have the option to participate in a variety of voluntary benefit plans including:
- A Choice of Medical Plan Options, some with Health Savings Account (HSA)
- Vision and Dental
- Life and AD&D Benefits
- Short and Long-Term Disability
- Hospital Indemnity, Accident, and Critical Illness Insurances
- Optional Identity Theft and Legal Protection Services
- Employee Referral Bonus Eligibility up to $10,000
- Mobility Among Wyetech-supported Contracts
- Various contract and work locations throughout Maryland, Virginia, Colorado, Texas, Utah, Alaska, Hawaii and OCONUS
- Various team-building events throughout the year such as monthly lunches, summer company picnic, and an annual holiday party.
- Employees receive two complimentary branded clothing orders annually.
Salary Range: $108.71 to $147.21 per hour
Hourly pay rates listed for this position serve as a general guideline and are not a guarantee of compensation. Compensation will vary dependent upon factors including but not limited to Government contract rates; education; relevant prior work experience, knowledge, skills, and competencies; certifications; and geographic location. Hourly pay rates reflect the pre-benefit gross wage amounts.
Wyetech, LLC 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 status as a protected veteran.
Affirmative Action Statement:
Wyetech, LLC is committed to the principles of affirmative action in all hiring and employment for minorities, women, individuals with disabilities, and protected veterans.
Accommodations:
Wyetech, LLC is committed to providing an inclusive and accessible hiring process. If you need any accommodations during the application or interview process, please contact Brittney Wood. at 844-WYETECH x727 or [email protected]. We are happy to provide reasonable accommodations to ensure equal access to all candidates.
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Salary Context
This $224K-$305K range is above the 75th percentile for Data Scientist roles in our dataset (median: $164K across 177 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 10,827 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Wyetech, 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 $190,000 based on 1,566 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($265K) sits 40% above the category median. Disclosed range: $224K to $305K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Wyetech AI Hiring
Wyetech has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Linthicum Heights, MD, US, Laurel, MD, US. Compensation range: $249K - $305K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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.
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