What State and Local IT Leaders Need to Know About Recruiting, Evaluating, and Retaining AI Professionals in 2026
Published April 2026 | By Overture Partners
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TL;DR — What This Guide Covers Demand for AI and data science professionals in state and local government has moved from aspirational to urgent. Agencies are deploying machine learning models, generative AI tools, and predictive analytics platforms — and discovering that the talent needed to build, govern, and maintain those systems is among the hardest to hire in any labor market. This guide covers: the six AI and data science roles in highest government demand, how to evaluate candidates you may not have the in-house expertise to assess, the compensation gap and how to bridge it, AI governance compliance requirements, and why contract staffing is often the most practical near-term path to building AI capability in government. |
State and local government agencies are implementing artificial intelligence in ways that would have seemed ambitious five years ago. Predictive models for Medicaid fraud detection. Generative AI chatbots for citizen service portals. Machine learning systems for infrastructure maintenance scheduling. Natural language processing tools for permit application processing.
These aren't pilot programs. Many are operational. And they share a common pressure point: the talent required to build, operate, govern, and improve them is in shorter supply — and higher demand — than almost any other category of IT professional in 2026.
For government hiring managers, this creates a compound challenge. AI and data science roles are among the most technically specialized positions in the IT market. They're also among the hardest to evaluate without deep domain knowledge. And the compensation gap between government and private sector for these roles is among the widest in any IT discipline.
This guide is designed to give government IT leaders the specific knowledge they need to hire well in this market — what the roles actually require, how to assess candidates, how to compete for talent, and how to use contract staffing to build AI capability on a realistic timeline.
Why AI and Data Science Hiring Is Different From Other IT Hiring
Most government IT roles have well-established competency frameworks, industry certifications, and predictable evaluation criteria. A network engineer either knows BGP routing or doesn't. A cybersecurity analyst either holds their CISSP or is working toward it. The hiring manager can assess fit against a defined standard.
AI and data science hiring doesn't work that way — for reasons that go beyond the rapid evolution of the field.
The Depth-Breadth Problem
AI practitioners sit along a spectrum from deeply theoretical (research scientists building novel models) to deeply applied (data engineers building pipelines that serve production systems). Government agencies almost always need the applied end of this spectrum — professionals who can build and deploy real systems against real operational requirements, not researchers publishing in academic venues. But job descriptions often aren't precise enough to signal this, and candidates don't always self-identify correctly along the spectrum.
Effective AI hiring in government requires being specific about what applied looks like in your environment: not 'experience with machine learning' but 'experience deploying and maintaining supervised classification models on tabular government datasets with defined accuracy and fairness thresholds.'
The Explainability Requirement
In the private sector, an AI professional's primary stakeholders are often other technical practitioners. In government, they are frequently policy directors, agency commissioners, legislators, and citizens — none of whom think in terms of model architecture or training pipelines. Government AI professionals must be able to translate technical work into language that supports decision-making at the policy level. This is a distinct skill that technical evaluation criteria alone won't surface — it has to be explicitly assessed.
The Governance Dimension
Government AI systems operate in a compliance and accountability environment that private sector deployments rarely match. Fairness audits. Explainability documentation. Bias testing across protected class categories. Agency-level AI governance policies. Emerging state-level AI legislation. A government AI professional who hasn't worked in this environment will often be unprepared for the documentation, oversight, and approval processes that government AI deployment actually requires. This experience dimension is frequently absent from standard AI hiring criteria.
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Hiring an AI professional who's only worked in a startup environment into a government role is like hiring someone who's only driven on private tracks to navigate rush hour traffic. The skills overlap, but the environment is different enough that the learning curve is real. |
The Six AI and Data Science Roles in Highest Government Demand
The following role profiles cover the positions state and local government agencies are most actively recruiting for in 2026. Each profile includes the government use cases driving demand, the core skills to evaluate, an honest assessment of hiring difficulty, and a note on contract staffing applicability.
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Machine Learning Engineer Government Use Cases: Fraud detection (Medicaid, tax, benefits); predictive maintenance for infrastructure; public safety risk scoring; benefits eligibility modeling Core Skills to Evaluate: Python/R proficiency; scikit-learn, TensorFlow, or PyTorch; feature engineering; model evaluation methodology; experience with production deployment (not just notebook environments); familiarity with tabular government datasets Hiring Difficulty in Government: Very High — one of the most competed-for roles in the entire tech labor market Contract Staffing Note: Contract staffing is the most practical near-term path. Senior ML engineers are rarely looking at government job boards; direct network outreach to professionals with prior public sector or mission-driven experience is essential. |
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Data Scientist Government Use Cases: Policy impact analysis; program outcome modeling; public health surveillance; resource allocation optimization; equity analysis of service delivery Core Skills to Evaluate: Statistical modeling; hypothesis testing; A/B experimental design; strong data visualization; SQL and Python fluency; experience communicating findings to non-technical audiences — this is the most underweighted criterion in most job descriptions Hiring Difficulty in Government: High — demand exceeds supply across sectors; government compensation constraints are a persistent barrier for senior-level candidates Contract Staffing Note: Contract engagements work well for project-defined analytical work. Data scientists who have worked in government or academic research environments are more likely to be open to government roles; target outreach accordingly. |
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AI Governance & Ethics Specialist Government Use Cases: AI use policy development; algorithmic bias auditing; explainability documentation; compliance with state-level AI legislation; responsible AI program management Core Skills to Evaluate: Understanding of fairness metrics (demographic parity, equalized odds, calibration); familiarity with NIST AI RMF; policy writing experience; legal or regulatory background a significant plus; ability to work across technical and non-technical stakeholders Hiring Difficulty in Government: Medium-High — an emerging role where the candidate pool is growing but still thin; government is actually more competitive here than in pure technical roles because the regulatory context gives government experience significant value Contract Staffing Note: Many agencies begin with a consulting or contract engagement to develop their AI governance framework before building a permanent function. This is a strong contract-first use case. |
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Prompt Engineer / GenAI Specialist Government Use Cases: Citizen-facing chatbot development; internal knowledge management systems; document summarization and analysis tools; legislative research automation; case worker support tools Core Skills to Evaluate: Prompt engineering and chain-of-thought design; RAG architecture familiarity; LLM evaluation methodology; experience with enterprise LLM deployment (not just consumer API calls); security awareness around prompt injection and data leakage Hiring Difficulty in Government: Medium — a newer role category where government agencies are actually better positioned than they expect; mission-driven candidates find government GenAI work compelling because the use cases are directly public-serving Contract Staffing Note: Contract placements are standard for this role. Project-based GenAI deployments align naturally with defined contract terms, and the field is moving fast enough that contract professionals often have more current knowledge than the permanent hiring pipeline would surface. |
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Data Engineer Government Use Cases: Data pipeline architecture for analytics platforms; data lake and data warehouse construction; ETL/ELT for legacy system integration; real-time data infrastructure for dashboards and monitoring systems Core Skills to Evaluate: Python/Spark/SQL fluency; cloud data platform experience (AWS GovCloud, Azure Government, Google Public Sector); ETL tool proficiency (dbt, Airflow, Informatica); experience with large-scale data quality and governance processes Hiring Difficulty in Government: High — data engineers are in demand across all sectors; government loses many candidates to private sector on compensation Contract Staffing Note: Contract-to-hire is a highly effective model for this role. Data engineers who are motivated by infrastructure scale (government datasets are genuinely large) and stability often become strong permanent hire candidates after an initial contract period. |
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MLOps / AI Infrastructure Engineer Government Use Cases: AI system deployment and monitoring; model versioning and registry management; inference infrastructure for production AI; CI/CD pipelines for ML models; cloud AI platform administration Core Skills to Evaluate: ML platform experience (MLflow, Kubeflow, Vertex AI, SageMaker GovCloud); containerization (Docker, Kubernetes); CI/CD pipeline development; monitoring and observability for deployed models; security hardening for AI infrastructure Hiring Difficulty in Government: Very High — a small candidate pool that is aggressively pursued by hyperscalers and well-funded private sector employers Contract Staffing Note: Contract staffing is often the only realistic near-term option. Government cloud platform experience (GovCloud, Azure Government) is a genuine differentiator that narrows the competitive field — target candidates with this background specifically. |
The Compensation Gap — and How to Bridge It
The salary differential between government and private sector for AI and data science roles is substantial — in many cases, 30 to 50 percent. That gap is real, and pretending it doesn't exist in recruiting conversations damages credibility. The more effective approach is to acknowledge it directly and then build the case for the full value of a government engagement.
AI & Data Science Compensation: Government vs. Private Sector (2026 Estimates)
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Role |
Government (Annual) |
Private Sector (Annual) |
IT Contract Rate (Hourly) |
Role |
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Data Scientist (Mid-Level) |
$95,000 – $125,000 |
$120,000 – $160,000 |
$85 – $110/hr |
Data Scientist (Mid-Level) |
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Senior Data Scientist |
$120,000 – $155,000 |
$150,000 – $200,000 |
$110 – $135/hr |
Senior Data Scientist |
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ML Engineer (Mid-Level) |
$105,000 – $135,000 |
$130,000 – $175,000 |
$95 – $120/hr |
ML Engineer (Mid-Level) |
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Senior ML Engineer |
$130,000 – $165,000 |
$160,000 – $220,000 |
$120 – $150/hr |
Senior ML Engineer |
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Data Engineer |
$90,000 – $120,000 |
$115,000 – $155,000 |
$85 – $115/hr |
Data Engineer |
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AI/ML Architect |
$145,000 – $185,000 |
$180,000 – $250,000 |
$130 – $150/hr |
AI/ML Architect |
Sources: NASCIO, CompTIA IT Industry Outlook, BLS OES data, Levels.fyi government sector estimates. Ranges reflect mid-career professionals (5–10 years experience) in major metro areas. Contract rates reflect W2 or 1099 hourly billing for IT contract professionals. Government figures include locality pay adjustments. Private sector figures reflect base compensation only, excluding equity and bonus.
What Actually Bridges the Gap for AI Candidates
The candidates most likely to accept government AI roles at government compensation levels share a profile: they are motivated by impact at scale, they have some prior public sector exposure, and they understand — or can be helped to understand — the full value of the government employment package. The bridge is not a single factor but a combination:
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What Government AI Work Offers |
How to Surface It in Recruiting |
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Data access at genuine population scale — government AI professionals work with datasets that no private company has |
Be specific: 'You'll work with 10 years of statewide Medicaid claims data — a dataset of a scale and completeness that no research institution or private employer can match' |
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Public impact that's visible and attributable — the fraud you detect, the services you optimize, the inequities you surface have direct citizen consequences |
Name the impact in the job description and the interview: connect the role's work to specific policy outcomes |
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Employment stability and pension value that compound over time |
Calculate and state the total compensation including pension contribution equivalent — it narrows the perceived gap significantly |
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Federal Public Service Loan Forgiveness (PSLF) — for candidates with student debt, this can be worth $50K–$150K in net lifetime value |
Surface PSLF proactively in recruiting conversations — most candidates haven't calculated what it's worth to them |
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Unique credentials in an emerging field — government AI governance experience is rare and increasingly valued as oversight requirements expand |
Frame government AI roles as career-building: 'This work will make you one of a small number of practitioners who have deployed AI in a regulated public sector environment' |
How to Evaluate AI and Data Science Candidates When You're Not One Yourself
One of the most common challenges government hiring managers face in AI and data science recruiting is not knowing enough about the technical domain to evaluate candidates confidently. This is a legitimate problem — and one that has practical solutions that don't require becoming an AI expert.
Principle 1: Evaluate Applied Judgment, Not Just Technical Knowledge
The most important differentiator between AI professionals who succeed in government environments and those who struggle is not their technical sophistication — it's their judgment about how to apply technical tools to real operational problems with real constraints. A candidate who has memorized every architecture decision in a transformer model but has never deployed anything to production in a regulated environment is not well-suited for most government AI roles.
Evaluation questions should focus on what the candidate has actually built, deployed, and maintained — not what they could theoretically build. The distinction between 'I've worked with gradient boosting models' and 'I deployed a gradient boosting fraud detection model to production, monitored it for drift over 18 months, and retrained it twice in response to distribution shift' is the entire difference between academic and applied experience.
Principle 2: Assess Communication to Non-Technical Audiences
Ask every AI candidate to explain one of their projects to you as if you were the agency director who has to sign off on deploying it. Pay attention to whether they can do this naturally — whether they reach for plain language, whether they understand what a non-technical decision-maker actually needs to know. This is a core job requirement for government AI professionals, and it surfaces immediately under even light examination.
Principle 3: Use a Structured Technical Screen with Subject Matter Input
If no one on your team has deep AI expertise, bring it in for the technical screen. Options include: a current AI contractor who can assess the candidate, a university partner with a data science faculty relationship, or a staffing partner (like Overture) who employs practitioners capable of conducting structured technical assessments. The investment in a 45-minute structured technical screen is far less costly than a mis-hire.
Interview Guide: Questions and What Strong Answers Look Like
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Interview Question |
What a Strong Answer Demonstrates |
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Walk me through a model you've built that went to production. What was the problem, what did you build, and how did you know it was working? |
Concrete problem description; specific technical choices with rationale; defined success metrics; post-deployment monitoring approach; awareness of failure modes. Candidates who describe only notebook experiments or proof-of-concept work are not production-ready. |
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How would you explain to a non-technical agency director why a model predicted a particular outcome for a specific case? |
Fluency with explainability concepts (SHAP values, LIME, feature importance); ability to translate technical language to plain language; understanding of why explainability matters in government contexts (fairness, due process, audit requirements). |
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Tell me about a time when your model produced results that were technically accurate but operationally problematic. What happened? |
Demonstrates real deployment experience; shows awareness of the gap between model performance metrics and operational impact; reveals problem-solving approach and accountability posture. Candidates with only academic experience often cannot answer this question from personal experience. |
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How do you test a model for bias or fairness issues before deployment? What would you do if you found a significant disparity across a protected class? |
Knowledge of fairness metrics appropriate to the problem type; awareness of government-specific obligations (civil rights, equal protection); practical approach to remediation; understanding of escalation and documentation requirements. This question is particularly important for roles involving benefits, services, or enforcement decisions. |
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If you joined this team and discovered that the data infrastructure we're using has significant quality issues that affect the reliability of any model trained on it, how would you approach that? |
Prioritization thinking; ability to work incrementally under constraints; communication approach to stakeholders; willingness to delay modeling work in favor of foundational data quality investment. Candidates who immediately propose building a model anyway are a red flag. |
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What's your experience working in environments with significant compliance, documentation, or approval requirements? How has that affected your work? |
Prior government, healthcare, or financial services experience is valuable context here; what matters most is the candidate's ability to work effectively within structured approval processes without treating them as obstacles to be circumvented. Frustration with compliance is a significant culture-fit risk in government AI roles. |
AI Governance and Compliance: What Government AI Contractors Need to Know
Government AI deployments operate in a compliance environment that is evolving rapidly. Federal guidance, state-level AI legislation, and existing data privacy frameworks all create obligations that AI contractors in government must understand and work within. Agencies that don't assess contractor familiarity with this environment before placement often discover the gap mid-project — at significant cost.
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Framework / Requirement |
What It Governs |
Contractor Implication |
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NIST AI RMF (AI 100-1) |
Voluntary framework for AI risk management across the system lifecycle: GOVERN, MAP, MEASURE, MANAGE |
Contractors should be familiar with the framework structure and able to produce documentation that supports agency AI RMF implementation |
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State AI Legislation |
Over 20 states have enacted or are advancing AI governance laws covering automated decision-making, transparency, and impact assessments |
Contractors must be briefed on the specific requirements in the agency's jurisdiction; compliance varies significantly by state |
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Executive Order on AI (Federal) |
Establishes requirements for federal AI use including safety testing, transparency, and equity evaluation — increasingly referenced by state policy as a baseline |
State agencies receiving federal funding may face alignment requirements; contractors should understand EO provisions relevant to the project scope |
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HIPAA / Health Data AI |
AI systems trained on or operating against PHI must comply with HIPAA privacy and security requirements; AI outputs may constitute disclosures requiring safeguards |
Require BAA execution; confirm that training data handling, model inference, and output logging comply with HIPAA minimum necessary standards |
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FERPA / Education Data AI |
AI systems that access or process student educational records are subject to FERPA — including model training on student data |
Contractors must understand FERPA-compliant data handling; written agreements specifying permitted uses of student data are required |
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CJIS / Criminal Justice AI |
AI systems involving criminal justice data require CJIS compliance for data access, processing, and storage |
Same requirements as any CJIS-covered IT role; AI models trained on CJIS data carry additional documentation and audit obligations |
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Civil Rights / Disparate Impact |
Government AI systems affecting benefits, enforcement, or services may be subject to civil rights review for disparate impact on protected classes |
Contractors should be prepared to conduct, document, and disclose disparate impact analyses; outputs must be reviewable by agency legal counsel |
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What to Confirm Before Placing an AI Contractor Does the contractor understand the relevant compliance frameworks for the data their model will touch? Have they produced AI documentation (model cards, data sheets, impact assessments) in prior engagements? Are they prepared to submit their work to an explainability or fairness audit if required? Do they understand that their model's outputs may be subject to public records requests, legal challenge, or legislative review? Have they worked in environments where AI deployment required a formal approval process? These questions should be asked in the interview, not assumed in the contract. |
Using Contract Staffing to Build AI and Data Science Capability in Government
The civil service hiring timeline — six months to a year for competitive IT roles — is fundamentally incompatible with AI project timelines. Most AI initiatives have defined deployment windows, compliance deadlines, or budget periods that can't wait for a permanent hire process to complete. Contract staffing is not a workaround in this context. It's the appropriate tool.
Why Contract Staffing Works Well for Government AI Roles
- AI work is naturally project-oriented. A machine learning model deployment, a data infrastructure build, or a GenAI pilot has a defined scope — making a contract term structure with clear deliverables a natural fit.
- The best AI professionals are typically employed. Reaching them requires direct outreach through specialist networks, not passive postings — which is the model good IT staffing partners operate on.
- Government compensation constraints are less limiting for contract rates, which are structured differently from civil service salary bands. This meaningfully expands the reachable candidate pool.
- The contract-to-hire pathway works particularly well for data engineers and data scientists, where an extended evaluation period reveals fit dimensions that a structured interview process can't fully assess.
What to Look for in an AI Staffing Partner
Not every IT staffing firm can place AI and data science professionals competently. The skill set is specialized enough that a staffing partner without genuine AI hiring experience will often submit candidates who look right on paper but aren't actually prepared for applied government AI work.
- Ask whether the firm has placed AI professionals in government environments previously — and what the outcomes were.
- Ask how they assess applied versus academic AI experience in the screening process.
- Ask whether their team includes practitioners who can conduct a technical screen, not just recruiters who can match keywords.
- Ask about their familiarity with government AI compliance requirements — NIST AI RMF, HIPAA data handling, FERPA, state-level AI legislation.
- Ask about their candidate engagement model after placement — do they maintain contact with placed contractors, and can they identify when a contract-to-hire conversion is appropriate?
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Overture Insight Overture Partners' approach to AI and data science staffing is built on the same Precise Talent Blueprint methodology we apply to all government IT roles — with additional emphasis on applied experience evaluation, governance literacy, and the ability to work effectively in a compliance-intensive environment. We maintain active relationships with AI and data science professionals who have prior public sector or mission-driven experience — the specific subset of this talent market that government agencies are most likely to successfully recruit and retain. If your agency is building AI capability and needs professionals who can operate effectively in a government environment from day one, we'd welcome a conversation. |
Writing AI and Data Science Job Descriptions That Attract the Right Candidates
Most government AI job descriptions are written for classification purposes, not candidate attraction. They list technologies, certifications, and years of experience in ways that are accurate but fail to communicate what the work actually involves or why a skilled AI professional should care about it. The result is a posting that attracts a narrow slice of candidates who are actively searching and deters the professionals most likely to be a strong fit.
Elements of an Effective Government AI Job Description
- Lead with the problem, not the title. 'We are building a machine learning system to identify patterns of Medicaid fraud that affect more than 2 million enrollees annually' is more compelling than 'Machine Learning Engineer, Grade 23.'
- Be specific about the technical environment. Name the actual platforms, data infrastructure, and deployment context. AI professionals assess job descriptions based on technical specificity — vague descriptions signal that the role may not be substantive.
- Separate required from preferred qualifications — and be honest about both. If a master's degree is listed as required but the work could be done well by an experienced practitioner without one, remove it. Unnecessary barriers shrink the applicant pool without improving quality.
- Name the governance and compliance environment explicitly. 'Work will involve development of explainable AI systems subject to state equity review and public records obligations' tells the right candidates that this is serious government work — and tells the wrong candidates to self-select out.
- Surface the value proposition in the description itself. Pension, loan forgiveness, data scale, mission impact — these belong in the job description, not just in the offer conversation.
- State the flexibility available. Remote-eligible, hybrid, or flexible schedule options should appear prominently — they materially expand the candidate pool for AI roles, where the most qualified professionals typically have multiple options.
Frequently Asked Questions
How can government agencies compete for AI and data science talent?
Government agencies compete for AI talent by leading with mission impact — the scale and significance of government data problems is a genuine differentiator for candidates motivated by meaningful work. Additional competitive factors include employment stability, student loan forgiveness (PSLF), funded certifications, and access to large-scale public datasets that are rare in the private sector. Contract staffing is also highly effective, allowing agencies to access top-tier AI professionals without the constraints of permanent civil service hiring.
What AI and data science roles are in highest demand in government in 2026?
The highest-demand roles in state and local government include machine learning engineers, data scientists, AI governance and ethics specialists, prompt engineers and GenAI specialists, data engineers, and MLOps infrastructure engineers. Demand is highest in agencies with active digital transformation initiatives, fraud detection programs, predictive analytics mandates, and generative AI deployments in citizen-facing services.
What compliance requirements apply to AI contractors in government?
AI contractors in government may be subject to HIPAA for health data, FERPA for education data, CJIS for criminal justice data, the NIST AI Risk Management Framework, emerging state-level AI governance legislation, and civil rights requirements around disparate impact analysis. The applicable frameworks depend on the agency type and the data the AI system will process. Contractors should be assessed for familiarity with these frameworks before placement, not briefed after the fact.
How do you evaluate AI and machine learning candidates for government roles?
Effective evaluation should assess applied deployment experience (not just academic or notebook work), ability to explain AI systems clearly to non-technical stakeholders, understanding of bias and fairness considerations, familiarity with government compliance requirements, and track record of working within structured approval processes. A structured technical screen — ideally conducted with subject matter input from a practitioner — should supplement the standard interview process.
What is responsible AI in government and why does it matter for hiring?
Responsible AI in government refers to practices that ensure AI systems are fair, transparent, accountable, and subject to human oversight — particularly where AI outputs affect citizens' access to services, benefits, or justice. It matters for hiring because government AI professionals must understand and apply responsible AI principles as an operational requirement, not a theoretical ideal. Candidates who cannot articulate bias testing, explainability requirements, and audit documentation are poorly suited to government AI roles.
Can government agencies use contract staffing to fill AI and data science roles?
Yes — and for most government agencies, contract staffing is the most practical near-term path to AI capability. The civil service hiring timeline is incompatible with most AI project timelines, and compensation constraints make competing for senior AI talent through permanent hiring particularly difficult. Contract rates are structured differently from civil service bands, expanding the reachable candidate pool. Contract-to-hire is especially effective for data engineer and data scientist roles, where an extended evaluation period surfaces fit dimensions that interviews cannot fully assess.
What is the NIST AI Risk Management Framework and how does it apply to government?
The NIST AI RMF (NIST AI 100-1) provides a voluntary framework for managing AI risk across the system lifecycle, organized around four core functions: GOVERN, MAP, MEASURE, and MANAGE. For government agencies, it provides a structured approach to AI documentation, risk assessment, and governance. Contractors working on government AI systems may be expected to produce documentation aligned with the framework and should be assessed for familiarity with its structure and requirements during the hiring process.
Conclusion: AI Talent Is the Limiting Factor — But It Doesn't Have to Be a Barrier
Government agencies are building AI systems that matter. The challenge is not technical — the platforms exist, the use cases are well-defined, and the data is available. The challenge is human: finding, evaluating, and retaining the professionals who can translate AI capability into operational government outcomes.
That challenge is real, but it's not insurmountable. The agencies making the most progress on AI staffing in 2026 share a set of practices: precise role definitions, honest compensation storytelling, applied-experience-first evaluation, governance literacy as a baseline requirement, and a willingness to use contract staffing as the primary near-term path to capability rather than waiting for a permanent hire process to deliver.
AI talent is competitive. But the government has something that no private employer can offer: work that demonstrably affects the lives of millions of people, on data that no private company can access, in an environment where the stakes of getting AI right are uniquely and visibly high. For the right candidates, that's not a consolation — it's the point.
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Work with Overture Partners Overture Partners specializes in placing AI, data science, and IT contract professionals in government environments. We understand what applied AI experience looks like, how to evaluate governance literacy, and how to find the candidates who are motivated by public-sector work — not just the ones who are available. If your agency is building AI and data science capability and wants a staffing partner who understands the technical and compliance environment you're working in, we'd welcome a conversation. |
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