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About This Role
Senior Data Scientist (Artificial Intelligence)
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### Federal Reserve Board – Division of Consumer and Community Affairs (DCCA)
Company: Diverse Agile Solutions (DAS)
Location: Washington, DC (Hybrid Preferred) \| Remote Considered
Job Type: Full\-Time Consultant
Clearance/Work Authorization: U.S. Citizenship Required
About Diverse Agile Solutions
=================================
Diverse Agile Solutions (DAS) is a certified Minority Business Enterprise (MBE) specializing in delivering innovative technology solutions and highly skilled IT professionals to federal, state, and commercial organizations. Our expertise spans Artificial Intelligence, Cloud Engineering, Data Analytics, Agile Transformation, DevSecOps, Cybersecurity, Enterprise Architecture, and Digital Modernization.
At DAS, we help organizations solve complex business challenges through emerging technologies while fostering innovation, collaboration, and continuous learning.
Position Overview
=====================
Diverse Agile Solutions is seeking a Senior Data Scientist (Artificial Intelligence) to support the Federal Reserve Board's Division of Consumer and Community Affairs (DCCA) as part of its newly established AI Lab.
This is an exciting opportunity to help build next\-generation Artificial Intelligence capabilities that improve consumer protection, regulatory oversight, community development, and operational efficiency through Generative AI and Machine Learning.
The ideal candidate is a full\-stack AI practitioner capable of owning the complete AI lifecycle—from research and experimentation to application development, deployment, monitoring, and production support. You will work closely with economists, attorneys, analysts, and senior leadership to design intelligent solutions that deliver measurable business value.
What You'll Do
==================
Artificial Intelligence \& Machine Learning Development
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- Research, design, and develop innovative AI and Machine Learning solutions supporting DCCA initiatives
- Build proof\-of\-concept AI applications and transition successful prototypes into production
- Develop Generative AI solutions using Large Language Models (LLMs)
- Design Retrieval\-Augmented Generation (RAG) architectures
- Implement prompt engineering strategies for enterprise AI applications
- Fine\-tune and evaluate foundation models for domain\-specific use cases
- Develop NLP solutions including:
+ Text classification
+ Named Entity Recognition (NER)
+ Information extraction
+ Document summarization
+ Semantic search
- Apply supervised, unsupervised, deep learning, and statistical modeling techniques
- Evaluate emerging AI frameworks and technologies for enterprise adoption
Full\-Stack AI Application Development
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- Build production\-ready AI applications using:
+ Python
+ Streamlit
+ Dash
+ Flask
+ R Shiny
- Develop intuitive dashboards and visual analytics applications
- Create interactive data visualizations using:
+ Plotly
+ Matplotlib
+ Seaborn
+ Tableau
+ Power BI
- Translate complex analytical findings into actionable business insights
Deployment \& MLOps
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- Deploy AI and ML applications into cloud environments
- Containerize applications using Docker
- Build CI/CD pipelines for AI deployments
- Implement model monitoring and observability
- Create automated retraining pipelines
- Manage model versioning and lifecycle management
- Optimize API integrations and AI inference costs
- Troubleshoot and maintain production AI applications
- Collaborate with Cloud Engineers and Infrastructure teams
Agile Collaboration
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- Participate in Agile ceremonies including:
+ Sprint Planning
+ Daily Standups
+ Sprint Reviews
+ Retrospectives
- Partner with business stakeholders to identify AI opportunities
- Translate business requirements into scalable AI solutions
- Present findings to executive leadership and technical teams
- Document code, methodologies, and technical decisions
- Mentor team members and contribute to the growth of DCCA's AI practice
Governance \& Responsible AI
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- Develop AI solutions aligned with federal security and governance standards
- Support FISMA, Privacy Impact Assessments, and ATO documentation
- Apply Responsible AI principles including:
+ Fairness
+ Bias detection
+ Explainability
+ Transparency
- Collaborate with security and compliance teams throughout the AI lifecycle
Required Qualifications
===========================
- U.S. Citizenship
- Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or a related field
+ Master's degree preferred
- Minimum 6 years of professional experience developing, deploying, and maintaining AI/ML applications
- Expert proficiency with Python or R
- Experience building production AI applications
- Experience deploying Machine Learning solutions into cloud environments
- Strong knowledge of:
+ Machine Learning
+ Deep Learning
+ Natural Language Processing
+ Generative AI
- Experience with:
+ Scikit\-learn
+ spaCy
+ XGBoost
- Experience developing interactive applications using:
+ Streamlit
+ Dash
+ Flask
+ R Shiny
- Experience creating dashboards and visualizations using:
+ Tableau
+ Power BI
+ Plotly
+ Matplotlib
+ Seaborn
- Experience with containerization and CI/CD
- Strong statistical modeling and analytical skills
- Excellent written and verbal communication skills
- Ability to independently deliver solutions from concept through production
Preferred Qualifications
============================
Candidates with experience in one or more of the following are highly encouraged to apply:
- Federal Government or Regulatory Agency experience
- Financial Services or Banking
- Consumer Finance
- Banking Supervision
- Agile methodologies (Scrum, Kanban)
- Jira or Azure DevOps
- Large Language Models (GPT, Llama, Nova)
- LangChain
- LlamaIndex
- Prompt Engineering
- Vector Databases
- Semantic Search
- AWS AI Services including:
+ Amazon Bedrock
+ SageMaker
+ Comprehend
+ Rekognition
+ Transcribe
- AWS deployment services:
+ EC2
+ ECS
+ Lambda
+ S3
+ CloudWatch
- Databricks
- Infrastructure as Code:
+ Terraform
+ CloudFormation
- MLOps
- Model Monitoring
- Automated Retraining
- Responsible AI
- AI Explainability
- Multi\-modal AI
- AWS Certifications
- Experience handling regulated or sensitive data
Technical Skills
====================
Programming Languages
- Python
- R
- SQL
AI / Machine Learning
- Generative AI
- Large Language Models (LLMs)
- Machine Learning
- Deep Learning
- NLP
- RAG
- Prompt Engineering
- Semantic Search
- Fine\-Tuning
Frameworks \& Libraries
- Scikit\-learn
- spaCy
- XGBoost
- LangChain
- LlamaIndex
- Streamlit
- Dash
- Flask
- R Shiny
Cloud \& DevOps
- AWS
- Docker
- Kubernetes
- CI/CD
- Git
- Terraform
- CloudFormation
Visualization
- Tableau
- Power BI
- Plotly
- Matplotlib
- Seaborn
- ggplot2
Why Join Diverse Agile Solutions?
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At DAS, you'll work alongside some of the industry's brightest technology professionals on innovative federal initiatives that leverage Artificial Intelligence to solve meaningful business challenges.
We offer:
- Competitive compensation
- Exciting federal technology programs
- Opportunities to work with cutting\-edge AI technologies
- Collaborative and innovative culture
- Professional development and certification support
- Exposure to enterprise\-scale cloud and AI platforms
- Opportunity to help shape the future of AI in the federal government
Work Environment
====================
- Full\-time consulting position
- Hybrid work environment (Washington, DC)
- Remote work may be considered
- Agile, collaborative AI Lab
- Fast\-paced innovation environment focused on research and rapid prototyping
Equal Employment Opportunity
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Diverse Agile Solutions is an Equal Opportunity Employer committed to building a diverse and inclusive workforce. We consider all qualified applicants for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other protected characteristic under applicable federal, state, or local law.
Apply Today
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If you're passionate about Artificial Intelligence, Machine Learning, and building production\-ready AI solutions that make a real impact, we'd love to hear from you.
Join Diverse Agile Solutions and help shape the future of AI innovation within the Federal Reserve Board.
Salary Context
This $156K-$166K range is above 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 Diverse Agile 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 ($161K) sits 16% below the category median. Disclosed range: $156K to $166K.
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.
Diverse Agile Solutions AI Hiring
Diverse Agile Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US. Compensation range: $166K - $166K.
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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