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About This Role
Job Description
ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
We are seeking a versatile Data Scientist / Machine Learning Engineer to join our team in Annapolis Junction, MD, leveraging artificial intelligence and advanced analytics to transform massive datasets into strategic mission insights. In this role, you will take full ownership of the AI/ML lifecycle—from designing mathematical models and writing automated Python workflows to deploying predictive models into cloud\-native processing platforms. You won't just build models in isolation; you will manipulate large\-scale datasets using PySpark and SQL, build compelling data visualizations, and work side\-by\-side with cross\-functional teams to tackle high\-stakes data challenges. If you thrive on blending computational mathematics, predictive modeling, and scalable machine learning to uncover hidden trends in large datasets, you’ll fit right in with Team Nyla.
The annual base salary range for this role is $152,000\-$180,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
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Required Skills
AI/ML Model Development \& Deployment: Hands\-on experience developing, training, validating, and deploying machine learning models in production environments.
Large\-Scale Data Processing: Practical background utilizing Apache Spark or cloud data platforms for distributed, large\-scale dataset manipulation.
Programming \& Querying Proficiency: Strong expertise in Python (including data science packages) and SQL for data modeling, extraction, and transformation.
Statistical \& Predictive Modeling: Background in advanced statistics, computational mathematics, and quantitative/qualitative analytical techniques.
Data Visualization \& Analytics Automation: Demonstrated skill creating automated analytics workflows and building intuitive dashboards/visualizations using Tableau, Power BI, or Python libraries.
Education: Bachelor’s Degree in Data Science, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline, PLUS 7\+ years of professional experience in data science, predictive modeling, and AI/ML engineering OR Master’s Degree in a quantitative discipline, PLUS 5\+ years of specialized experience developing and deploying machine learning models on large datasets OR High School Diploma / GED, PLUS 11\+ years of hands\-on technical experience in data science, advanced analytics, and predictive modeling in lieu of a degree.
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Desired Skills
Target Knowledge / OCP Experience: Target knowledge or direct experience with OCP (Operational Capability Packages / Operational Target Frameworks).
Cloud Data Architectures: Experience leveraging cloud\-native analytics services (e.g., AWS SageMaker, EMR, Databricks) for distributed model training and data storage.
Cross\-Functional Collaboration: Strong history of collaborative problem\-solving within multi\-disciplinary engineering and intelligence teams.
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About Nyla Technology Solutions
Nyla Technology Solutions delivers exceptional Artificial Intelligence (AI), Data Science, and Software Engineering services for the U.S. Government. Nyla embraces a forward\-thinking and bold approach at every turn, earning us a solid reputation of technical trendsetters within the industry. We have a passion for developing solutions that have a quick and immediate impact on mission. Headquartered in Columbia, Maryland, our customers love how we tackle their most challenging problems and get things done.
If you have the unique experience and expertise we are seeking, along with the desire and determination to invest your time and energy as a part of Nyla’s team,
Taking Care of All of You
Nyla provides a top\-of\-market compensation and benefits package. And through our unique Nyla FLEX program, we custom tailor these benefits to best fit your lifestyle.
The Nyla FLEX benefit program is designed to offer you flexibility in the 3 biggest areas of your life: your pay, your leave, and your schedule.
PAY \- Nyla starts with 4 weeks of Annual Leave plus 11 holidays and an additional day of Annual Leave for each year you’re at the company. You have the flexibility to cash out your annual leave hours, opt out of other Nyla benefits, and/or arrange for additional hours on contract\* (over 40 hrs/week). There’s even an option to earn 1\.3 times your hourly rate once you work over 1880 hours on contract!
LEAVE \- Want to spend more time with the family? Want more time to travel the world? You can BUY additional annual leave for a total of 6 weeks of annual leave. That’s up to 240 hours of leave plus 11 holidays! That’s not even including paid anniversary leave!
SCHEDULE \- Does the traditional 40\-hour workweek no longer fit your lifestyle? With Nyla FLEX, you have the freedom to scale down to 30\-32 hours while still enjoying the top\-notch Nyla benefits you know and love. It's flexibility that works for you without compromising the perks!
WHAT ABOUT OTHER BENEFITS? Nyla’s health care (medical, dental, and vision) is 100% covered by the company. We provide 10% 401k matching \- with full vesting day 1! Our Professional Development offers $5,000 per year to be used towards fees, tuition, or time off for your continued growth. We even have a student loan repayment program and we provide 8 hours of volunteering annually so you can support your community, making your world a better place.
To learn more about Nyla's culture and our exceptional benefit packages click here.
Nyla is an equal opportunity employer.
Salary Context
This $152K-$180K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Nyla Technology Solutions, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($166K) sits 14% below the category median. Disclosed range: $152K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Nyla Technology Solutions AI Hiring
Nyla Technology Solutions has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Annapolis Junction, MD, US. Compensation range: $140K - $180K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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