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About This Role
Description
ICF is a mission-driven company filled with people who care deeply about improving the lives of others and making the world a better place. Our core values include Embracing Difference, and we seek candidates who are passionate about building a culture that encourages, embraces, and hires dimensions of difference.
Our Health Engineering Systems (HES) team partners with customers to turn data into clear, actionable insights. We are seeking a Junior Data Scientist to support data exploration, analysis, modeling, validation, and visualization using PostgreSQL, SQL, Python, Databricks, and the DataConnect reporting environment using AWS QuickSight.
This role is well suited for an early-career professional who is curious, detail-oriented, comfortable learning new technologies, and able to communicate findings to both technical and non-technical stakeholders.
Responsibilities
- Use SQL and Python to extract, clean, transform, and analyze structured data from PostgreSQL and other approved sources.
- Perform exploratory data analysis to identify patterns, trends, anomalies, and data-quality issues.
- Assist with the development, testing, and documentation of statistical analyses, machine-learning models, and analytical workflows.
- Develop and support notebooks and repeatable analytical workflows in Databricks.
- Create and maintain clear visualizations, datasets, and reports for DataConnect using AWS QuickSight.
- Validate analytical outputs and document assumptions, methods, limitations, and results.
- Partner with data engineers to define data needs, resolve quality issues, and improve the usability of analytical datasets.
- Translate business questions into measurable analyses with guidance from senior team members and stakeholders.
- Write reusable, version-controlled code and basic unit tests for analytical functions and data transformations.
- Present findings through concise written summaries, visualizations, and team demonstrations.
- Follow established standards for data privacy, security, governance, and responsible use of data.
- Participate in Agile planning, refinement, peer reviews, and continuous learning activities.
- Other duties as assigned.
Minimum Qualifications
- Bachelor's degree
- 2+ years of experience using Python for data analysis, data preparation, and statistical modeling
- Candidate must be able to obtain and maintain a Public Trust clearance.
- Candidate must reside in the United States, be authorized to work in the United States, and have lived in the United States for three full years out of the last five years.
What you will bring
- Bachelor's degree data science, statistics, mathematics, computer science, engineering, economics, or a related quantitative field.
- Academic, internship, project, or professional experience using Python for data analysis.
- Foundational SQL skills, including joins, aggregations, filtering, and data validation.
- Understanding of core statistics, data preparation, exploratory analysis, and model-evaluation concepts.
- Ability to communicate analytical methods and findings clearly in writing and presentations.
- Ability to work collaboratively, manage assigned tasks, and incorporate feedback.
- Experience with PostgreSQL or another relational database platform.
- Experience using Databricks, Spark, notebooks, or cloud-based analytics platforms.
- Experience creating data visualizations or dashboards with AWS QuickSight or a comparable business-intelligence tool.
- Experience with common Python data-science libraries such as pandas, NumPy, scikit-learn, or PySpark.
- Familiarity with TypeScript and its use in data applications, dashboards, services, or integrations.
- Familiarity with Git, code reviews, unit testing, CI/CD, or Agile development practices.
- Exposure to data governance, metadata, data quality, privacy, or responsible AI practices.
- Experience working with health, public-sector, or other regulated data environments.
Job Location: This position requires that the job be performed in the United States. The candidate must reside in the United States, be authorized to work in the United States, and perform all work in the United States. ICF monitors employee work locations, blocks access from foreign locations and foreign IP addresses, and prohibits personal VPN connections.
Bot and Third-Party Applications
Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact [email protected] for assistance.
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:
$81,499.00 - $138,549.00 Nationwide Remote Office (US99)
Salary Context
This $81K-$138K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At ICF, 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 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 42% below the category median. Disclosed range: $81K to $138K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
ICF AI Hiring
ICF has 7 open AI roles right now. They're hiring across MLOps Engineer, AI Product Manager, Data Scientist, AI/ML Engineer. Positions span Reston, VA, US, Montgomery, AL, Rockville, MD, US. Compensation range: $125K - $184K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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