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About This Role
Ignite Digital enables national security agencies to accelerate decisions, elevate operational outcomes and achieve the outsized performance only an inside partner can deliver.
We combine mission experience, domain knowledge and technology expertise with the things partners can only know by being there every day. When agencies need a combination of tech and touch, Ignite Digital creates outcome\-driving partnerships and stands with agencies to accomplish mission objectives.
Ignite Digital accelerates capability to the speed of every mission, deploying intelligent solutions and AI integration for faster, better transformation. Ignite delivers partnership beyond presentations to inspire the confidence to lead in a digital\-forward world.
Ignite Digital. The Edge from Within.
Perks of Working at Ignite Digital:
- Competitive pay and benefits, including PTO
- Education stipends and referral bonuses
- Compelling work with the U.S. federal government
- Strong emphasis on volunteer and community engagement
- Opportunity to shape the future of our industry
- Supportive colleagues and management who invest in your growth
Ignite Digital Services is hiring a Data Scientist to independently develop and deploy AI/ML models in support of a large federal enterprise multi\-domain data ecosystem. You will operate with autonomy, drive innovation, and contribute to mission\-critical data science solutions, while delivering within a larger team project management environment.
Key Responsibilities:
- Independently build, train, and fine\-tune AI/ML models.
- Apply data science methodology to extract insights and create predictive capabilities.
- Analyze complex datasets and identify trends.
- Select and implement ML algorithms for specific problems.
- Develop feature engineering and model validation approaches.
- Prepare models for production deployment and support stakeholders in interpreting outputs.
- Create interactive dashboards and visualizations.
- Contribute to data governance, security, and automation initiatives.
- Collaborate with subject matter experts and AI/ML engineers.
Required Skills:
- Proficiency in Python, SQL, and ML frameworks.
- Experience with NLP, LLM, or GenAI tools (e.g. LoRA, LangChain, RAG, LLM Fine Tuning).
- Hands\-on experience with data visualization tools (e.g. Qlik, Power BI, Tableau, Databricks).
- Strong quantitative and analytic abilities.
Desired Skills:
- Experience designing and implementing custom Generative AI solutions.
- Familiarity with cloud platforms (AWS, Azure, GCP).
- Experience working with federal clients.
Clearance Requirements:
- U.S. Citizen, Secret or TS/SCI eligible.
Education \& Experience:
- Bachelor's degree in computer science, engineering, mathematics, or related field.
- 3\+ years relevant professional experience.
- Intermediate certifications (CCSP, CFR, FITSP\-M, GSEC, Security\+, SSCP) or advanced certifications (SecurityX / CASP\+, GCSA, GSLC, CISSP).
Work Location: National Capital Region
- On\-client site with potential for hybrid telework.
Internal Salary Guidance:
- Maximum salary for T\&M Rate card: $125,000
Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
Ignite Digital is a Small Business committed to providing exceptional service to government agencies at competitive prices. The capabilities and experience of our staff and our extensive industry relationships distinguish Ignite Digital Services among government contractors.
Equal Opportunity Employer/Veterans/Disabled
For individuals who would like to request an accommodation, please visit https://bit.ly/2XqZoLM (CA) or https://bit.ly/3Eo922f (SC) or contact Human Resources. Ignite Digital Services will not make any posting or employment decision that does not comply with applicable laws relating to labor and employment, equal employment opportunity, employment eligibility requirements or related matters. Nor will Ignite Digital Services require, in a posting or otherwise, U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract.
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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 Ignite Digital, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Ignite Digital AI Hiring
Ignite Digital has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Washington, DC, US.
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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