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About This Role
Job ID: 5836
City: Remote
State: Remote
Job Type:
Compensation: $0 to $0
Position Summary
Centuyrion is hiring an AI Senior Data Scientist to help our client in establishing an AI Lab to explore and implement generative AI and machine learning solutions that enhance staff productivity, improve analytical capabilities, and strengthen the Division's
work in consumer protection and community development. We are looking for a full\-stack Senior Data Scientist to support the AI Lab's research, development, and implementation of AI/ML solutions, with emphasis on generative AI applications. This role requires end\-to\-end ownership, from exploratory research and model development through application deployment and production maintenance. The ideal candidate is comfortable working across the full technology stack: building models, creating visualizations, developing applications, and deploying solutions to on\-prem and/or cloud infrastructure.
The AI Lab operates as a small, agile team where practitioners are expected to move between research, development, and deployment activities. This position will contribute to strategy while doing hands\-on technical work, building models, training systems, evaluating performance, and deploying solutions. The AI Lab collaborates closely with DCCA's Data Analytics and Risk and Surveillance sections, and coordinates with the Board's enterprise technology on infrastructure, governance, and compliance matters.
Responsibilities
AI/ML and Generative AI Development
- Research, design, and develop machine learning and artificial intelligence solutions to support DCCA's mission, with emphasis on generative AI applications
- Build and iterate on proof\-of\-concept AI solutions that demonstrate value for specific use cases, transitioning successful prototypes into production applications
- Design and implement applications leveraging large language models for text analysis, summarization, information extraction, document classification, and workflow automation
- Develop prompt engineering strategies and retrieval\-augmented generation (RAG) systems to improve AI application performance
- Experiment with fine\-tuning, model customization, and evaluation techniques to optimize AI solutions for DCCA use cases
- Evaluate emerging AI technologies, frameworks, and models to identify opportunities for adoption within DCCA workflows
- Apply advanced statistical and machine learning techniques including supervised/unsupervised learning, classification, regression, and deep learning methods
Deployment and Operations
- Build, deploy, and maintain AI/ML models and applications in cloud environments (AWS, Kubernetes, or internal analytics platforms), working collaboratively with AI Cloud Engineers when available or independently managing end\-to\-end deployment
- Develop interactive dashboards and analytical applications using Python frameworks (Streamlit, Dash, Flask) or R Shiny; leverage AI\-assisted development tools to rapidly prototype and iterate on data products
- Create data visualizations and user interfaces using Python libraries (Plotly, Matplotlib, Seaborn), R (ggplot2\), Tableau, Power BI, or similar tools that translate analytical outputs into intuitive, actionable insights for non\-technical audiences
- Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI application deployments with API integrations, rate limits, and cost optimization
- Implement monitoring, logging, alerting, and visual dashboards for model performance, data quality, and system health; establish automated retraining pipelines and model versioning strategies
- Troubleshoot and maintain deployed applications, addressing performance issues, ensuring scalability, and updating applications as requirements evolve
- Support governance requirements including documentation for security assessments, privacy reviews, and compliance obligations related to deployed systems
Collaboration, Communication and Agile Practices
- Work within a light agile framework, participating in sprint planning, standups, and retrospectives to coordinate work with team members
- Break down technical work into manageable tasks, estimate effort, track progress, and communicate status, blockers, and technical challenges to stakeholders
- Work directly with DCCA program staff economists, analysts, attorneys, and senior leadership to understand business needs, identify AI/ML opportunities, and translate requirements into technical solutions
- Communicate technical concepts effectively to both technical and non\-technical
audiences through presentations, reports, and executive summaries
- Document technical work, including code, methodologies, and project outcomes to support knowledge sharing and project continuity
- Contribute to building an AI/ML practice within DCCA, including documentation, capability development, and mentoring team members
Governance and Compliance Awareness
- Work within federal IT governance frameworks including FISMA, privacy, and records management requirements as they apply to AI systems
- Coordinate with the Board's security, privacy, and compliance functions on matters related to AI Lab systems and applications
- Apply responsible AI practices including fairness evaluation, bias detection, model interpretability, and transparency in model development
- Maintain awareness of AI ethics, accountability, and appropriate use considerations in federal regulatory contexts
- Support preparation of documentation for system security plans, privacy impact assessments, and authority to operate processes when required
Required Qualifications
- U.S. citizenship
- At least six years of hands\-on experience developing, deploying, and maintaining AI/ML applications within a large, professional, or academic organization
- Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or related technology field (Master's degree preferred) Expert proficiency in Python or R for data science development; experience with additional programming languages
- Production deployment experience: Demonstrated ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices
- Application development: Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, RShiny, or similar
- Data visualization: Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools to communicate technical
concepts to non\-technical audiences
- AI/ML expertise: Advanced knowledge of machine learning, NLP (text normalization, Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with frameworks such as Scikit\-learn, Spacy, XGBoost
- Statistical analysis: Advanced knowledge of statistical modeling, data analysis techniques, and problem\-solving skills
- Ability to work independently and collaboratively, taking ownership of solutions from conception through production deployment
Preferred Qualifications
- Prior experience in U.S. federal government, regulatory, supervisory, or policy environments
- Experience with financial services data, consumer finance, banking supervision, or regulatory data
- Experience working within agile frameworks (Scrum, Kanban) and project tracking tools (Jira, Azure DevOps)
- Experience with LLM APIs (GPT, Llama, Nova) and frameworks (LangChain, LlamaIndex); knowledge of prompt engineering, fine\-tuning, vector databases, and semantic search
- Familiarity with AWS AI services (Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe)
- Experience building production\-grade web applications with advanced user interfaces; knowledge of data storytelling and visual design principles
- Experience visualizing model performance metrics, feature importance, and model explainability outputs
- Hands\-on experience with AWS deployment services (EC2, ECS, Lambda, S3, CloudWatch), Databricks, and infrastructure as code (Terraform, CloudFormation)
- AWS certifications (Solutions Architect, Machine Learning Specialty, or similar)
- Familiarity with MLOps practices including model monitoring, versioning, automated retraining, and deployment pipelines
- Experience with multi\-modal AI applications; understanding of responsible AI practices (bias detection, fairness evaluation, model interpretability)
- Familiarity with federal IT governance frameworks (FISMA, privacy requirements) and application security in regulated environments
- Experience working with sensitive or regulated 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 Centurion Consulting Group, 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.
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.
Centurion Consulting Group AI Hiring
Centurion Consulting Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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