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About This Role
Overview:
Edgewater Federal Solutions is seeking a highly skilled Senior Data Scientist to supporting our client as part of its newly established AI Lab. This innovative team is focused on researching, developing, and deploying advanced Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that improve operational efficiency, enhance analytical capabilities, and support the division's consumer protection and community development mission.
The Senior Data Scientist will serve as a full\-stack AI practitioner responsible for the complete lifecycle of AI/ML solutions—from research and model development through deployment, monitoring, and production support. This role requires hands\-on expertise in Generative AI, data science, application development, visualization, cloud deployment, and MLOps practices. The successful candidate will thrive in a collaborative, agile environment and possess the ability to translate complex business challenges into scalable AI\-driven solutions. This work can be performed remotely, and does require US Citizenship, this role is for a 6 month long period and not anticipated to be extended.Responsibilities:
AI/ML \& Generative AI Development* Research, design, and develop Artificial Intelligence and Machine Learning solutions supporting DCCA business objectives.
- Build proof\-of\-concept AI solutions and transition successful prototypes into production environments.
- Design and implement applications utilizing Large Language Models (LLMs) for document processing, summarization, classification, workflow automation, and information extraction.
- Develop and optimize prompt engineering strategies and Retrieval\-Augmented Generation (RAG) architectures.
- Evaluate, customize, fine\-tune, and validate AI models to improve accuracy and business value.
- Apply advanced statistical and machine learning methodologies, including:
- + Supervised and unsupervised learning
+ Classification and regression modeling
+ Deep learning techniques
+ Natural Language Processing (NLP)
Application Development, Deployment \& Operations* Build, deploy, and maintain AI/ML applications across cloud and on\-premises environments.
- Develop interactive dashboards and analytical applications using frameworks such as Streamlit, Dash, Flask, or R Shiny.
- Create compelling visualizations and user interfaces using tools such as Tableau, Power BI, Plotly, Matplotlib, Seaborn, and ggplot2\.
- Implement CI/CD pipelines, containerization strategies, and automated deployment processes using Docker and related technologies.
- Establish monitoring, logging, alerting, model performance tracking, and automated retraining capabilities.
- Optimize GenAI deployments, including API management, rate limiting, and cost controls.
- Troubleshoot application and infrastructure issues while ensuring system scalability and reliability.
- Support security, privacy, compliance, and governance requirements for deployed solutions.
Collaboration \& Stakeholder Engagement* Participate in Agile ceremonies, including sprint planning, standups, and retrospectives.
- Collaborate directly with economists, analysts, attorneys, technical teams, and executive leadership.
- Translate business requirements into scalable AI and data science solutions.
- Communicate technical concepts effectively to both technical and non\-technical audiences.
- Prepare technical documentation, knowledge transfer materials, reports, and executive briefings.
- Mentor team members and contribute to the growth of DCCA's AI/ML capabilities and best practices.
Governance \& Responsible AI* Support compliance with federal governance frameworks, including FISMA, privacy requirements, and records management standards.
- Coordinate with security, privacy, compliance, and enterprise technology stakeholders.
- Apply Responsible AI principles, including fairness, transparency, interpretability, accountability, and bias mitigation.
- Assist with development of security documentation, privacy impact assessments, and Authority to Operate (ATO) packages where required.
Qualifications:
Education* Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, Artificial Intelligence, or a related technical discipline.
- Master's degree preferred.
Experience* Minimum of six (6\) years of experience developing, deploying, and maintaining AI/ML solutions in enterprise, government, academic, or research environments.
- Demonstrated experience owning projects from concept through deployment and operational support.
- Experience working independently in highly collaborative cross\-functional environments.
Technical Skills* Expert\-level proficiency in Python and/or R.
- Extensive experience with machine learning and statistical modeling frameworks.
- Strong experience developing and deploying AI/ML applications in cloud environments.
- Experience with:
- + Generative AI technologies and Large Language Models
+ Prompt engineering and RAG architectures
+ Application development frameworks (Streamlit, Dash, Flask, R Shiny)
+ Data visualization tools and libraries
+ Docker and containerized deployments
+ CI/CD practices and DevOps fundamentals
+ NLP technologies including Named Entity Recognition (NER), POS tagging, embeddings, and text analytics
- Experience with frameworks such as:
- + Scikit\-learn
+ SpaCy
+ XGBoost
+ Other modern AI/ML frameworks
Clearance \& Eligibility* U.S. Citizenship required.
- Ability to successfully obtain and maintain any required Federal Reserve Board background investigation.
Preferred Qualifications* Experience supporting federal government, regulatory, or policy\-focused organizations.
- Experience working with banking, financial services, consumer finance, or regulatory data.
- Knowledge of Agile methodologies, Scrum, Kanban, Jira, and Azure DevOps.
- Experience with:
- + GPT, Llama, Nova, and similar LLM APIs
+ LangChain and LlamaIndex
+ Vector databases and semantic search solutions
+ Amazon Bedrock, SageMaker, Comprehend, Rekognition, and Transcribe
+ EC2, ECS, Lambda, S3, CloudWatch
+ Databricks
+ Terraform and CloudFormation
- Knowledge of MLOps best practices, automated retraining, monitoring, and model governance.
- Experience building production\-grade web applications and advanced user interfaces.
- Familiarity with explainable AI, fairness evaluation, and model interpretability.
- Understanding of multimodal AI technologies.
- AWS certifications such as:
- + AWS Solutions Architect
+ AWS Machine Learning Specialty
+ Other relevant cloud certifications
- Experience handling sensitive, regulated, or protected data.
Salary: $134,000 \- $139,000Additional benefits include:* Paid Time Off \& Holiday Pay
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Disability, Life Insurance, and AD\&D
- Flexible Spending Accounts
- Pre\-Tax 401K and/or After\-Tax Roth IRA (with employer matching contribution)
- Tuition and Technical Training Reimbursement
- Exercise Reimbursement
- Computer Reimbursement
- Employee Assistance Program
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.* Regularly required to sit, stand, talk, hear, and use hands and fingers to operate a computer, keyboard, telephone, and standard office equipment.
- Specific vision abilities required include close vision for computer\-based work.
- Occasionally required to lift and/or move up to 15 pounds.
- Fine hand manipulation and extended keyboard usage required.
Work Environment
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.* Exposure to general office conditions while conducting office duties.
- Moderate noise environment typical of a business office.
- Ability to work in a confined office environment.
- Ability to sit at a computer terminal for extended periods of time.
About Us:
Edgewater Federal Solutions is a privately held government contracting firm located in Frederick, MD. The company was founded in 2002 with the vision of being highly recognized and admired for supporting customer missions through employee empowerment, exceptional services and timely delivery. Edgewater Federal Solutions is ISO 9001, 20000\-1, 270001 certified, appraised at CMMI Level 3 Maturity for Development and Services, and has been named in the Top Workplaces in the Greater Washington Area Small Companies for 2018 through 2025\.
It has been and continues to be the policy of Edgewater Federal Solutions to provide equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, marital status, veteran status, and/or other statuses protected by applicable law.
Salary Context
This $134K-$139K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Edgewater Federal 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($136K) sits 29% below the category median. Disclosed range: $134K to $139K.
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.
Edgewater Federal Solutions AI Hiring
Edgewater Federal Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US. Compensation range: $139K - $139K.
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.
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