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About This Role
BlueFlag is looking for a Senior Data Scientist to support a premier data and AI platform for the Department of Veterans Affairs. You'll work directly with internal clients to turn operational problems into production machine learning and AI solutions on Databricks in Azure, and help their teams adopt modern cloud tooling. This role is hands-on. We want someone who has taken models from a notebook to production and kept them running.
What You'll Do
- Consult with internal clients to frame business problems as analytical or ML problems, define success metrics, and set realistic scope
- Build data products and workflows that support critical operations, from source data discovery through production deployment
- Migrate and modernize client workloads from legacy environments (including R, Stata, and SAS) to Python and Databricks, with measurable gains in runtime, cost, or reliability
- Develop, train, and validate ML models (classification, regression, forecasting, clustering, anomaly detection) for complex business problems
- Engineer features and build reusable, governed feature pipelines on large datasets with PySpark and SQL
- Track experiments, register models, and manage model versions and promotion through MLflow
- Deploy models for batch scoring and real-time serving, and automate retraining with scheduled jobs and CI/CD pipelines
- Monitor models in production for performance, data drift, and data quality, and set thresholds and alerts that trigger review or retraining
- Lead AI adoption with clients, including LLM and agentic use cases such as retrieval-augmented generation (RAG), document summarization, and classification
- Evaluate LLM and agent outputs for accuracy, groundedness, and safety before and after release
- Document models (purpose, data, assumptions, limitations, evaluation results) so they hold up to governance and responsible AI review
- Build reference use cases with tutorials, reference code, and training to drive adoption of cloud tooling for concrete business results
- Host office hours and pair with client analysts and data scientists to upskill them
- Present findings and model results clearly to technical and non-technical audiences, including leadership
Why Join BlueFlag
At BlueFlag, we're passionate about leveraging cutting-edge technology to make a real difference. You'll be at the forefront of cloud innovation, working on projects that directly impact people's lives. We offer a high-growth, entrepreneurial environment that values fresh ideas and authentic teamwork.
If you're ready to take your data engineering career to new heights and contribute to meaningful projects that push the boundaries of technology, we want to hear from you. Join BlueFlag and be part of a team that's shaping the future of AI solutions!
Requirements
- Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Systems, Business, or a related scientific or technical discipline, and 15+ years of experience (or commensurate experience)
- Proficient in Python (pandas, NumPy, SciPy, scikit-learn) and advanced SQL (window functions, CTEs, query tuning) for data analysis
- 2+ years of hands-on work on a leading cloud data platform such as Databricks, Azure, AWS, or GCP
- Experience across the end-to-end data science workflow, from finding and assessing datasets to production deployment, including experiment tracking and model management with MLflow or similar
- Solid grounding in traditional machine learning: supervised and unsupervised methods, gradient-boosted trees (XGBoost, LightGBM), model selection, cross-validation, and hyperparameter tuning
- Strong applied statistics: hypothesis testing, regression, sampling, and experimental design
- Experience working with large datasets in a distributed environment (Spark/PySpark)
- Sound model evaluation practice: picking the right metrics, handling class imbalance, avoiding leakage, and explaining model behavior (for example SHAP or feature importance)
- Working knowledge of large language models (LLMs) and agentic AI workflows, including prompt design and RAG patterns
- Version control with Git and collaborative development practices (code review, branching, testing)
- Ability to explain technical work to non-technical stakeholders and turn ambiguous requests into defined deliverables
- US Citizen: Must be a citizen of the United States
- Security Clearance: Must be able to obtain a public trust clearance. Must be eligible to work in the United States.
Desired
- 5+ years as a data scientist, shipping multiple products that run in operation
- Working experience with Databricks in Azure, including Unity Catalog, Delta Lake, Databricks Jobs, and Databricks notebooks/Repos
- MLOps experience across the lifecycle, such as:
- + Model Serving endpoints for real-time inference + Feature engineering and feature tables governed in Unity Catalog + CI/CD for ML with Azure DevOps or GitHub Actions, and Databricks Asset Bundles + Production monitoring for drift and model quality (for example Lakehouse Monitoring) + Champion/challenger or A/B testing of models in production + Automated retraining and model lineage and auditability
- Experience with Azure AI services (Azure OpenAI, Azure Machine Learning) or Mosaic AI (Vector Search, Agent Framework)
- Prior experience shipping products that use LLMs or AI agents, including evaluation and guardrails
- 2+ years building visual insights (dashboards and reports) that support operational needs, with Power BI, Databricks AI/BI dashboards, or Tableau
- Prior experience with R, Stata, or SAS
- Experience refactoring R, Stata, or SAS codebases to Python, including validating that results match
- Experience with VA or federal healthcare data, and handling PHI/PII under federal privacy and security requirements
- Familiarity with federal AI governance and responsible AI practices (bias testing, model documentation, human oversight)
- Experience training or mentoring analysts and data scientists
- Master's or PhD in a quantitative field
Benefits
- Competitive salary
- Generous annual leave and paid holidays
- Comprehensive group health and dental plans
- 401(k) with company match
- Life insurance and AD&D coverage
- Ongoing training and professional development opportunities
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 BlueFlag LLP, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
BlueFlag LLP AI Hiring
BlueFlag LLP has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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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