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About This Role
About Blue Water Thinking
Guided by our principles of value generation, continuous innovation, customer\-centricity, and vested collaboration, Blue Water Thinking proudly supports our Federal clients in achieving their Agency goals.
Founded by a decorated twenty\-eight year Veteran of the United States Army, Blue Water Thinking understands and supports the mission of our Nation's war fighters, Veterans, their families and caretakers.
Leveraging our executive team's military experience and private and public sector consulting expertise, Blue Water Thinking takes an integrated "one\-team" approach and brings to bear best\-fit solutions, thought leadership, and grit to meet our client's transformational needs.
Fueled by our values of integrity, respect, professionalism, stewardship and customer service, the Blue Water Thinking team understands the power of the human connection, collaboration, humility and loyalty to one another, our clients and industry partners.
Lastly, our formula for success is simple: Build something good, take care of our people, keep our clients satisfied, nurture our work ethic and reputation, build long\-lasting partnerships, enjoy what we do and give back as much as possible.
Job Description:
Blue Water Thinking is seeking an experienced and dedicated Lead Data Scientist to join our team, supporting the Integrated Healthcare Transformation (IHT) initiative at the Department of Veterans Affairs. The primary responsibility of this role to provide the necessary capabilities and industry expertise in advanced analytics, predictive modeling, and machine learning initiatives supporting clinical decision\-making and operational optimization.
Responsibilities:
- Serves as technical lead for advanced analytics, predictive modeling, and machine learning initiatives supporting clinical decision\-making and operational optimization.
- Designs and validates algorithms for interoperability workflows, clinical informatics systems, and Agile integration reporting.
- Ensures compliance with VA data governance, security, and privacy standards.
- Present analytical outcomes to managers and stakeholders to guide business model adjustments and future planning.
- Translate data into meaningful reports that support healthcare transformation goals.
- Collaborate with cross\-functional teams to align data strategies with program objectives.
- Support predictive modeling and trend analysis to improve veteran healthcare delivery.
- Responsible for tasks such as predictive analytics, data migration fidelity, and clinical decision supports.
Minimum Qualifications:
- Master's degree in data science, computer science, health informatics, statistics, or a related quantitative field.
- Minimum of 10 years of experience in data analysis or advanced analytics.
- Minimum of 5 years applying machine learning and predictive modeling in healthcare or federal health IT environments.
- Demonstrated expertise in supporting PI Planning, backlog analysis, velocity metrics and other Agile processes.
- Demonstrated experience in interoperability standards (e.g. HL7, FHIR) and clinical terminology (SNOMED, LOINC).
- Demonstrated experience in data visualization tools, including PowerBI and Tablueau.
- Demonstrated expertise in ETL processes.
- Demonstrated expertise in integrating clinical informatics systems (Oracle Health, VISTA, CPRS) and validating data migration fidelity.
- Demonstrated experience in programing languages (SQL, Python, R) and and frameworks (TensorFlow, PyTorch).
- Demonstrated ability to interpret complex datasets and communicate findings to non\-technical audiences.
Preferred Qualifications:
- Certified Health Data Analyst (CHDA), Certified ScrumMaster (CSM) or SAFe Practitioner, Healthcare Data Science or AI certifications are highly desirable
- Ph.D. in health informatics, data science, machine learning, or related discipline.
- Experience supporting federal healthcare programs, especially within the Department of Veterans Affairs.
Eligibility:
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Must be able to obtain and maintain the required federal public trust clearance for this role
Compensation:
Salary for this position is determined by various factors, including but not limited to, location, the candidate's particular combination of knowledge, skills, competencies and experience, as well as contract specific affordability and organizational requirements. The proposed salary range for this position is outlined below.
Salary range: $145,000 \- $200,000
Benefits:
Blue Water Thinking offers a comprehensive benefits package including health insurance (medical, dental and vision), paid time off, federal holidays, and matching 401K plan.
This is a fantastic opportunity to be part of an impactful communications team, supporting the VA in its efforts to transform healthcare for veterans. If you're passionate about communications and want to contribute to a meaningful cause, we encourage you to apply!
Our Commitment to Equal Employment Opportunity.
Blue Water Thinking, LLC (BWT) is committed to equal employment opportunity. We recruit, employ, train, compensate, and promote without regard to race, religion, color, national origin, age, sex, disability, protected veteran status, or any other basis protected by applicable federal, state, or local law.
Applying for this Job:
- Resume must be submitted in word document format and must include dates in each section (experience, education, certifications...)
- Candidates must fill out the below form to the best of their knowledge
Salary Context
This $145K-$200K range is above the median for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At blue water Thinking, 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 $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($172K) sits 16% below the category median. Disclosed range: $145K to $200K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
blue water Thinking AI Hiring
blue water Thinking has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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