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About This Role
Description: NOTE: This opportunity is full-time employment position only (no 1099 or C2C engagements, or third parties or staffing agencies, please). The candidate MUST be a U.S. Citizen or Permanent Resident (Green Card holder). This is a remote opportunity; candidate must be based in the U.S.; have resided in the U.S. for at least 3 years in the past 5 years; ET time zone work schedule.
Daily Responsibilities
- Analyzes unstructured and semi-structured data, applying creativity to large-scale analysis for high-value use cases using advanced algorithms in distributed and cloud-based infrastructures. s. Utilizes advanced tools for interpreting complex data, delivering recommendations for business decisions. Experience in software development, data transport APIs, Cloud-based tools, and visual analytics, with expertise in open-source stacks, Windows development, and various data analysis technologies.
- Execute and advance the enterprise data science and AI strategy aligned to organizational goals, serving as a trusted advisor on advanced analytics, machine learning, and AI adoption.
- Lead high-impact AI/ML initiatives across business and technology teams, delivering proofs of concept and MVPs that mature into scalable production solutions.
- Translate complex business challenges into analytical frameworks and scalable AI-driven solutions that support strategic decision-making.
- Design, develop, and deploy advanced machine learning solutions, including predictive modeling, forecasting, NLP, large language models (LLMs), recommendation systems, optimization models, RAG, and other AI-powered applications.
- Apply advanced data science techniques including deep learning, ensemble methods, time series analysis, experimentation, A/B testing, and statistical modeling.
- Lead hands-on model development in Python, establishing best practices for reusable code, testing, reproducibility, feature engineering, and utilization of modern data science frameworks and libraries.
- Partner with AI and engineering teams to implement end-to-end MLOps practices, including model versioning, automated training and deployment pipelines, monitoring, drift detection, and continuous model improvement.
- Collaborate with data engineers and architects to build scalable data platforms, pipelines, and cloud-based solutions that support large-scale structured and unstructured data.
- Establish and enforce standards for model validation, explainability, interpretability, data quality, governance, responsible AI, bias mitigation, transparency, and auditability.
- Communicate complex analytical insights to executive and non-technical stakeholders through effective data storytelling, visualization, and strategic recommendations.
- Mentor and develop data science talent while leading ross-functional teams to deliver high-impact data science and AI solutions.
Requirements:
- Must of a U.S. Citizen or Permanent Resident (Green Card holder), as mandated by our government client.
- Must be able to complete/pass/hold at a minimum a Public Trust Investigation / background check. An active Public Trust or higher is preferred.
- Must be based / reside in the U.S.
- 10+ years of experience in data science, machine learning, or applied AI with deep expertise in machine learning, deep learning, and LLM-based approaches.
- 6+ years of demonstrated experience leading enterprise-scale data science initiatives.
- Expert-level proficiency in Python for data science and machine learning, including hands-on Python experience delivering production-grade data science solutions.
- Proven experience building and deploying ML models in production environments.
- Strong experience with MLOps tools, pipelines, and lifecycle management.
- Experience with LLMs, NLP, or generative AI applications.
- Experience in AI governance, model risk management, or ethical AI.
- Proven experience implementing MLOps frameworks and production ML systems (e.g., MLflow, Kubeflow, Azure ML, or SageMaker).
- Experience with big data tools (e.g., Spark) and cloud platforms (AWS, Azure, GCP).
- Strong SQL skills for data extraction, transformation, and analysis.
- Proficiency with data visualization and BI tools (e.g., Power BI, Tableau).
- Familiarity with federal AI governance frameworks, including the NIST AI Risk Management Framework and OMB AI guidance.
- Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention.
- Experience with generative AI tooling, including RAG frameworks, embedding models, and vector databases.
- Strong foundation in statistics, experimentation design, and model evaluation (including precision, recall, F1 score, and related performance metrics.
- Ability to process high-volume data collections and streams, making discoveries in the realm of big data.
- Requires strong technical and computational skills for coding, designing, and deploying sophisticated applications in unstructured data analysis.
- Excellent analytical skills, attention to detail, and strong problem-solving abilities.
- Excellent communication and collaboration skills to communicate complex analytical insights to executive and non-technical stakeholders. Ability to translate ambiguous business questions into analytical solutions.
- BS or MS degree (preferred) in data science, computer science, statistics or related field.
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. $145k -$175k.
Salary Context
This $145K-$175K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Powerhouse Institute, Inc, 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 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 16% below the category median. Disclosed range: $145K to $175K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Powerhouse Institute, Inc AI Hiring
Powerhouse Institute, Inc has 2 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer. Based in Washington, DC, US. Compensation range: $175K - $189K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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