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About This Role
Overview:
The Senior Data Scientist is a senior technical leader responsible for executing and advancing the advanced analytics, machine learning, and AI strategy across the organization. This role focuses on applied data science at enterprise scale, including model development, experimentation, and operationalization. The role emphasizes deep Python\-based modeling expertise, leadership of end\-to\-end ML lifecycle and MLOps, and delivery of scalable AI solutions (including large language models) that drive measurable business and mission outcomes for HUD programs (e.g., housing analytics, fraud detection, and intelligent document processing).
Responsibilities:
- Execute and advance the enterprise data science and AI strategy aligned to organizational goals
- Serve as a trusted advisor on advanced analytics, machine learning, and AI adoption
- Lead high\-impact AI/ML initiatives across business and technology teams
- Deliver time\-boxed proofs of concept and MVP solutions that establish foundational AI capabilities and mature into production systems
- Translate complex business problems into analytical frameworks and scalable solutions
- Design, develop, and deploy advanced machine learning models, including predictive modeling and forecasting, NLP and large language models (LLMs), and recommendation systems and optimization models
- Apply advanced techniques such as deep learning, ensemble methods, and time series analysis
- Develop and scale modern AI solutions including Retrieval\-Augmented Generation (RAG) and LLM\-based workflows and applications
- Ensure models are robust, explainable, and production\-ready
- Lead hands\-on model development using Python as the primary programming language
- Build high\-quality, reusable code for data processing and feature engineering, model development and evaluation, and experimentation and statistical analysis
- Establish best practices for Python\-based data science development, including code quality, testing, and reproducibility
- Utilize core libraries such as Pandas, NumPy, Scikit\-learn, PyTorch/TensorFlow
- Partner with the Senior AI Engineer to operationalize end\-to\-end MLOps practices, including model versioning, tracking, and reproducibility, automated training and deployment pipelines, model monitoring, drift detection, and performance management
- Ensure continuous delivery and improvement of models in production
- Partner with engineering teams to productionize models while maintaining data science ownership of model integrity
- Establish standards for experimentation, A/B testing, and model validation
- Partner with data engineers and architects to build scalable data pipelines and platforms
- Define best practices for data preparation, feature engineering, and data quality
- Work with large\-scale structured and unstructured datasets in cloud environments
- Ensure alignment between data science solutions and enterprise data architecture
- Establish best practices in model validation, explainability, and interpretability
- Ensure responsible AI practices including bias detection and mitigation
- Support model risk management and governance frameworks
- Promote transparency and auditability in AI/ML systems
- Communicate complex analytical insights to executive and non\-technical stakeholders
- Influence decision\-making through data storytelling and visualization
- Mentor and develop data scientists and analysts
- Lead cross\-functional teams delivering high\-impact data science solution
- Expert\-level proficiency in Python for data science and machine learning (required)
- Deep expertise in machine learning, deep learning, and LLM\-based approaches
- 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)
- 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
- Ability to translate ambiguous business questions into analytical solutions
- Strong communication and stakeholder engagement skills
- 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
Qualifications:
- US citizenship required
+ Public Trust preferred
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related field
- 10\+ years of experience in data science, machine learning, or applied AI
- Demonstrated experience leading enterprise\-scale data science initiatives
- Experience establishing data science or AI capabilities in organizations early in their AI maturity preferred
- Extensive 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 mentoring and leading data science teams
- Experience in AI governance, model risk management, or ethical AI
- Prior leadership role on federal programs (e.g., Lead Architect, Chief Engineer, Technical Director) preferred
- Experience with HUD or federal civilian agencies preferred
- Prior experience in consulting or client\-facing environments preferred
Target Pay Range: The below listed pay range for this position is not a guarantee of compensation or salary. The final offered salary will be influenced by a host of factors including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at Pyramid Systems that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits, to include our Employee Stock Ownership Program, FlexPTO, and learning and development opportunities. Pyramid Min: USD $145,560\.00/Yr. Pyramid Max: USD $210,000\.00/Yr. Why Pyramid?: Pyramid Systems, Inc. is an award\-winning, technology leader, driving digital transformation across federal agencies. We empower forward\-thinking innovations, accelerate production\-ready software, and deliver secure solutions so federal agencies can meet their mission goals. Voted a Top Workplace, both regionally (Washington, DC) and Nationally (USA) the past 2 years (2023 and 2024\) based on the feedback from our employees, we are headquartered in Fairfax, VA. and have a growing national footprint. We value and promote our Flexible Workplace approach because of the positive impacts it has on work\-life integration. We remain committed to ensuring every employee’s voice is heard, performance and results are recognized and rewarded, development and advancement is a focus, and diversity, equity and inclusion is a company priority. We offer competitive compensation and benefits (including a recently launched Employee Stock Ownership Plan \- ESOP), a robust performance\-based rewards program, and we know how to have fun! Our people and culture have endured and delivered for our clients for nearly three decades. EEO Statement: Pyramid Systems, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Salary Context
This $145K-$210K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Pyramid Systems 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($177K) sits 10% below the category median. Disclosed range: $145K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pyramid Systems Inc AI Hiring
Pyramid Systems Inc has 4 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist, AI/ML Engineer. Based in US. Compensation range: $157K - $210K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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