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About This Role
Description
*Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award.*
We are seeking a Senior Project Manager to lead data science services supporting a federal government client’s trade, analytics, and mission transformation priorities. This role will manage a multidisciplinary team of data scientists, data analysts, data engineers, data architects, and user experience professionals delivering analytical support, data\-driven solutions, and machine learning\-enabled capabilities for complex mission and operational challenges.
The ideal candidate brings strong federal project management experience, a background in applied analytics or data science programs, and the ability to work closely with senior government leaders, stakeholders, technical teams, and delivery partners. This role requires a leader who can translate mission gaps and process inefficiencies into actionable project plans, oversee implementation of analytical products, and ensure contractual obligations, quality standards, schedules, and performance expectations are met.
Job Location
This role will primarily support work in the Washington, DC Metro area, with work expected at or near the client site in Ashburn, VA as required by the contract. Telework may be permitted only with prior government approval.
If you accept this position, you should note that ICF does monitor employee work locations and blocks access from foreign locations/foreign IP addresses and also prohibits personal VPN connections.
Security Requirement
Must be a U.S. Citizen with a recent DHS Public Trust, DoD Secret, or DoD Top Secret clearance, and must be able to obtain and maintain a CBP background investigation or other required government suitability determination.
What You Will Do
- Lead the data science services contract team in providing analytical support to address complex business questions and mission needs
- Meet with program leaders, government stakeholders, and technical teams to identify priorities, understand business processes, and guide delivery of innovative data analysis techniques
- Collaborate with data scientists, analysts, engineers, architects, and application developers to support development of data science reports, machine learning models, dashboards, and other analytical solutions
- Assist the team in articulating mission gap problems, process inefficiencies, and operational challenges that can be addressed through data science, business intelligence, or automation
- Oversee implementation of analytical solutions recommended by the team, including data science reports, machine learning models, visualizations, and decision\-support products for agency leaders and stakeholders
- Manage project scope, staffing, schedules, risks, deliverables, and performance to ensure contractual obligations are fulfilled and quality standards are met
- Develop, maintain, and communicate work plans, project schedules, roadmaps, backlogs, status reports, and other management artifacts
- Provide expert direction and guidance to subordinate consultants, data scientists, and technical team members
- Serve as the primary point of contact to the Contracting Officer’s Representative and support ongoing coordination with the government Program Manager and other stakeholders
- Manage and control project funds and resources in accordance with contract requirements
- Support Agile delivery activities, including sprint planning, backlog management, prioritization, stakeholder demonstrations, and continuous improvement
- Ensure technical concepts, analytical findings, project risks, and delivery recommendations are communicated clearly to both technical and non\-technical audiences
- Support workforce transformation activities by helping teams present data science concepts, analytical products, and lessons learned to large audiences and varied stakeholder groups
What You Will Bring (Must haves)
- U.S. Citizenship required due to federal contract requirements
- Recent DHS Public Trust, DoD Secret, or DoD Top Secret clearance required
- Ability to obtain and maintain a CBP background investigation or other required government suitability determination
- Eight (8\) years of relevant experience in applied research, big data analytics, statistics, applied mathematics, data science, computer science, operations research, or another closely related quantitative, mathematical, scientific, or technical discipline
- At least five (5\) years of supervisory experience
- Advanced degree, such as a Master’s or Ph.D., in Statistics, Applied Mathematics, Data Science, Computer Science, Operations Research, or another closely related scientific or technical discipline
Preferred Qualifications
- PMP, PMI\-ACP, CSM, SAFe, or other project, program, or Agile delivery certification
- Experience managing federal data science, analytics, business intelligence, machine learning, or digital transformation programs
- Experience supporting DHS, CBP, or other federal law enforcement, trade, revenue, regulatory, or mission operations environments
- Experience leading multidisciplinary teams that include data scientists, data analysts, data engineers, data architects, UX/UI designers, application developers, cloud engineers, and DevSecOps personnel
- Experience managing projects involving machine learning, predictive analytics, natural language processing, graph analytics, entity resolution, anomaly detection, risk scoring, or advanced data visualization
- Experience translating stakeholder needs, mission gaps, and operational problems into project plans, implementation roadmaps, analytical requirements, and measurable outcomes
- Experience overseeing delivery of analytical products using Agile, Scrum, SAFe, or similar iterative delivery approaches
- Experience managing delivery in secure, compliant federal IT or data environments
- Experience with project management tools such as Jira, Confluence, Azure DevOps, MS Project, or similar tools
- Experience supporting executive briefings, stakeholder workshops, user engagement sessions, product demonstrations, or communities of practice
- Familiarity with cloud\-based analytics environments, data pipelines, data warehouses, data lakes, or modern data platforms
- Ability to apply broad management skills and specialized functional and technical expertise to guide a project team in delivering client solutions and managing day\-to\-day operations
- Ability to manage and control project funds and resources and serve as point of contact to the COR
Professional Skills
- Ability to plan, organize, direct, and control a project or program to ensure all contractual obligations are fulfilled, quality standards are met, and performance expectations are achieved
- Ability to provide expert direction and guidance to subordinate staff, develop schedules, and formulate work plans
- Demonstrated exceptional oral and written communication skills
- Strong leadership skills with the ability to manage multidisciplinary teams in a fast\-paced federal delivery environment
- Strong communication skills with the ability to explain technical concepts, analytical findings, risks, and recommendations to non\-technical stakeholders
- Ability to build trusted relationships with government leaders, CORs, program managers, product owners, technical staff, and end users
- Strong organizational skills with the ability to manage competing priorities, evolving requirements, and multiple concurrent workstreams
- Ability to identify project risks, resolve issues, remove blockers, and maintain delivery momentum
- Detail\-oriented with a focus on quality, schedule adherence, documentation, and customer satisfaction
- Strong problem\-solving skills and ability to guide teams from ambiguous mission needs to actionable delivery plans
- Collaborative, proactive, and mission\-focused with a commitment to delivering high\-quality analytical solutions for government stakeholders
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Working at ICF
ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting\-edge technology to help clients solve their most complex challenges, navigate change, and shape the future.
We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy.
We will consider for employment qualified applicants with arrest and conviction records.
Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email [email protected] and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act.
Candidate AI Usage Policy
At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in\-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process.
However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at [email protected]. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.
Pay Range \- There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.
The pay range for this position based on full\-time employment is:
$108,006\.00 \- $222,169\.00
Ashburn, VA (VA28\)
Salary Context
This $108K-$222K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ICF, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $108K to $222K.
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.
ICF AI Hiring
ICF has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ashburn, VA, US. Compensation range: $222K - $222K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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