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About This Role
Job Family:
Data Science \& Analysis Travel Required:
None Clearance Required:
Ability to Obtain Public TrustWhat You Will Do:
- Design, develop, implement, and maintain AI and machine learning solutions that improve healthcare, public health, biomedical research, and operational decision\-making.
- Identify opportunities to improve organizational efficiency by applying AI, automation, and advanced analytics to streamline business processes, reduce manual effort, and enhance workforce productivity.
- Design and implement AI\-enabled workflow solutions for document processing, knowledge management, literature reviews, information retrieval, case management, reporting, decision support, and other operational functions.
- Develop Generative AI (GenAI) solutions using large language models (LLMs), retrieval augmented generation (RAG), semantic search, vector databases, AI agents, and prompt engineering techniques.
- Develop predictive, classification, clustering, natural language processing (NLP), computer vision, and other machine learning models using structured, semi\-structured, and unstructured data.
- Develop scalable data science pipelines using Python, SQL, Spark, and cloud\-native analytics platforms.
- Evaluate, validate, and monitor AI models for performance, explainability, fairness, bias, reproducibility, and operational effectiveness.
- Collaborate with epidemiologists, researchers, engineers, program staff, and business stakeholders to identify AI use cases, prioritize opportunities, and translate business needs into production\-ready AI solutions.
- Develop dashboards, visualizations, technical documentation, presentations, and demonstrations that communicate AI capabilities and analytical findings to technical and non\-technical audiences.
- Support deployment, monitoring, and continuous improvement of AI and machine learning solutions using modern MLOps and DevSecOps practices.
- Apply responsible AI principles, governance, privacy, security, and human\-in\-the\-loop review throughout the AI lifecycle.
- Mentor junior team members and provide technical leadership, solution design, and code reviews as appropriate.
What You Will Need:
- Bachelor's degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, Public Health, or related field (or equivalent professional experience).
- Experience developing AI and machine learning solutions using Python and common data science libraries such as Pandas, NumPy, Scikit\-learn, TensorFlow, PyTorch, or similar frameworks.
- Experience applying AI to automate workflows, improve business processes, or enhance operational efficiency.
- Experience developing Generative AI applications using large language models (LLMs), prompt engineering, retrieval augmented generation (RAG), or similar techniques.
- Experience developing statistical models, predictive analytics, classification, clustering, regression, or natural language processing (NLP) solutions.
- Strong SQL skills and experience working with relational databases and cloud\-based data platforms.
- Experience preparing, integrating, transforming, and analyzing structured and unstructured data to support AI, machine learning, and advanced analytics solutions.
- Experience deploying AI or machine learning solutions into production environments.
- Understanding of responsible AI principles, model evaluation, explainability, governance, and human oversight.
- Experience working in Agile software development environments and fast\-paced, matrixed organizations
- Strong analytical, problem\-solving, written, and verbal communication skills.
- Ability to work independently and collaboratively in multidisciplinary technical teams.
- U.S. Citizenship required.
- Ability to obtain and maintain a Public Trust or higher federal security clearance, as required.
What Would Be Nice To Have:
- Experience implementing AI copilots, intelligent assistants, AI agents, workflow automation, or enterprise knowledge management solutions.
- Experience with Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Vertex AI, Microsoft Copilot Studio, LangChain, LangGraph, LlamaIndex, Hugging Face, or similar AI frameworks.
- Experience with Databricks, Snowflake, Microsoft Fabric, Azure AI Foundry, Palantir Foundry, or other enterprise AI and analytics platforms.
- Experience with MLOps platforms such as MLflow, Azure ML, SageMaker, Kubeflow, or similar technologies.
- Experience supporting healthcare, public health, biomedical research, life sciences, surveillance, or real\-world evidence (RWE) initiatives.
- Experience working with healthcare interoperability standards such as FHIR, HL7, LOINC, SNOMED CT, ICD\-10, or OMOP.
- Experience implementing AI governance frameworks, model risk management, human\-in\-the\-loop validation, and AI assurance processes.
- Experience supporting federal agencies such as CDC, HHS, NIH, CMS, FDA, or VA.
- Experience in consulting environments supporting multidisciplinary technical teams.
- Relevant AI, cloud, or data platform certifications.
The annual salary range for this position is $85,000\.00\-$141,000\.00\. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental \& Vision Insurance
- Personal and Family Sick Time \& Company Paid Holidays
- Parental Leave
- 401(k) Retirement Plan
- Group Term Life and Travel Assistance
- Voluntary Life and AD\&D Insurance
- Health Savings Account, Health Care \& Dependent Care Flexible Spending Accounts
- Transit and Parking Commuter Benefits
- Short\-Term \& Long\-Term Disability
- Tuition Reimbursement, Personal Development, Certifications \& Learning Opportunities
- Employee Referral Program
- Corporate Sponsored Events \& Community Outreach
- Care.com annual membership
- Employee Assistance Program
- Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
- Position may be eligible for a discretionary variable incentive bonus
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1\-571\-633\-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
*Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.*
Salary Context
This $85K-$141K range is in the lower quartile 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 Guidehouse, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($113K) sits 41% below the category median. Disclosed range: $85K to $141K.
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.
Guidehouse AI Hiring
Guidehouse has 6 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span New York, NY, US, Chicago, IL, US, Washington, DC, US. Compensation range: $124K - $216K.
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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