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About This Role
The Principal Data Scientist is a senior practitioner leader who operates at the intersection of hands\-on analytical and modeling execution, applied research, client\-facing solutioning, and cross\-functional program leadership. This role is designed for a data scientist who can walk into any project environment, immediately understand what question needs to be answered and why, sequence the analytical work, align the teams, and deliver.
At the Principal level, this person drives data science and AI/ML strategy for the organization, not just a single project. They set modeling standards, evaluate methodological and platform trade\-offs, lead reference architecture decisions for analytical and AI systems across engagements, and are the person RS21 turns to when a modeling or analytical decision is hard. They translate ambiguous client requirements into rigorous, defensible analytical approaches, own the full data science lifecycle from problem framing through model deployment and monitoring, and bridge the communication gap between business stakeholders, product teams, data engineers, and platform engineers with equal fluency.
This role further serves as an embedded technical program lead, with the discipline to decompose ambiguous initiatives into structured, sequenced delivery work, the systems thinking to connect every analytical task to its business outcome, and the ownership to keep multi\-workstream programs on track independently.
As a people manager, the Principal Data Scientist holds direct line management responsibility for a team of data scientists. They own hiring, performance management, career development, and day\-to\-day people leadership for their team, ensuring data scientists are growing technically while also being effectively staffed and supported across client engagements.
Critically, the Principal Data Scientist is a force multiplier. They raise the capabilities of those around them, train and coach junior and mid\-level staff, establish the patterns and practices RS21's data science function grows from, and actively contribute to RS21's business development and proposal efforts as a credible technical voice.
Key Responsibilities
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People Management
- Serve as the direct line manager for a team of data scientists, owning staffing, workload balance, and day\-to\-day people leadership.
- Conduct regular 1:1s, set goals, and deliver formal performance reviews and feedback that support each team member's growth and accountability.
- Own hiring decisions for the team, including interviewing, candidate evaluation, and onboarding planning for new data scientists.
- Identify and address performance issues proactively and fairly, partnering with HR and technical leadership as needed.
- Build individual development plans that align team members' career goals with RS21's technical roadmap and project needs.
Data Science \& Modeling
- Drive RS21's data science and modeling strategy, evaluate statistical, machine learning, and AI methodologies across engagements and make organization\-wide recommendations.
- Design, build, and validate production\-grade predictive, statistical, and machine learning models that address well\-defined business and operational questions.
- Architect end\-to\-end modeling workflows with rigorous validation, bias and performance monitoring, and reproducibility built into the design from day one.
- Establish modeling standards, experimentation practices, and analytical norms that apply across RS21's project portfolio.
- Ensure model reliability, fairness, and performance across engagements, and hold teams accountable to those standards.
LLM Enablement \& Applied AI
- Evaluate and select foundation model and modeling strategies for RS21's AI and LLM\-powered offerings; guide ethical AI approach across engagements.
- Design and implement analytical approaches that support LLM and AI use cases, including:
- Model evaluation, fine\-tuning, and prompt\-based experimentation
- Retrieval\-augmented generation (RAG) design and evaluation from a modeling perspective
- Statistical and human\-in\-the\-loop evaluation of generative AI outputs
- Lead methodology decisions; drive evaluation and experimentation strategy for AI\-powered systems across projects.
- Ensure rigor, transparency, and governance for AI\-powered analytical systems, including bias detection and model risk assessment.
- Optimize modeling approaches and feature strategies to support efficient, explainable AI and ML systems.
Cloud \& ML Platform Architecture (AWS)
- Set RS21's cloud data science strategy, evaluate platform trade\-offs, drive AWS ML platform decisions, and contribute to reusable reference architectures for modeling, experimentation, and AI\-ready platforms.
- Architect and leverage AWS services to support data science and AI workloads across SageMaker, Bedrock, Redshift, Athena, EMR, Glue, Lambda, and Step Functions.
- Lead model governance architecture, including experiment tracking, model registries, and access controls for sensitive analytical assets.
- Partner with data engineering and platform teams to ensure secure, cost\-effective, and scalable model deployment and serving infrastructure.
- Drive MLOps and automation practices; lead reliability strategy for model training, deployment, and monitoring pipelines.
Solutions Architecture \& Client Engagement
- Lead discovery and requirements\-gathering engagements with clients to translate ambiguous business and operational questions into concrete, defensible analytical and AI approaches.
- Serve as RS21's primary technical face in client\-facing data science settings, capable of presenting to executive stakeholders and technical teams in the language each audience needs.
- Produce modeling approach documents, solution design documents, and technical roadmaps that guide both client delivery and internal product development.
- Assess and document client analytical maturity and readiness for AI and ML adoption; identify gaps and prescribe actionable remediation paths.
- Own the technical narrative during solutioning, from pre\-sales and scoping through delivery kickoff and handoff.
Technical Program Leadership
- Own end\-to\-end technical execution planning for data science workstreams. Define the sequence of work, identify dependencies, and ensure delivery milestones map to both technical and business outcomes.
- Operate as a technical program lead within project delivery: decompose complex initiatives into structured Jira epics, stories, and tasks with clear acceptance criteria; understand how every ticket fits into the larger program arc.
- Establish and continuously improve RS21's delivery standards for data science programs, translating lessons learned across engagements into stronger project management practices organization wide.
- Partner with project managers and product owners to ensure the analytical execution plan stays aligned with contractual, operational, and business constraints.
- Lead planning, estimation, and review ceremonies with the technical authority to drive hard decisions to resolution when they arise.
- Serve as the central coordination point between client stakeholders, product teams, data engineers, platform engineers, and developers, translating across all languages with fluency.
Product \& Data Readiness Support
- Support the evolving analytical and AI architecture behind RS21's product capabilities, including predictive and real\-time ML systems.
- Assess and improve internal and client data and analytical readiness for AI and ML adoption.
- Serve as the connective tissue across client stakeholders, product, platform engineering, data engineering, and DevOps teams, moving fluidly between business language and technical depth depending on who is in the room.
Staff Development \& Org Capability
- Shape RS21's data science talent strategy, anticipate capability gaps before they become program risks, and partner with technical leadership on the hiring, development, and structural decisions needed to close them.
- Train, mentor, and grow junior and mid\-level data scientists in both technical depth and analytical thinking.
- Build the onboarding frameworks, internal playbooks, and knowledge\-transfer practices that make RS21's data science capability portable, consistent, and independent of any single person.
- Conduct model reviews, methodology reviews, and design critiques that elevate team output quality and raise the floor of what RS21 ships.
- Model big\-picture thinking, help the team understand not just what to build, but why it matters and how it connects to client outcomes and RS21's broader technical strategy.
Collaboration \& Communication
- Collaborate closely with developers and data engineers building AI\-powered features to ensure models and analytical outputs meet application and data requirements.
- Shape how RS21 communicates with data, influencing clients and executives through evidence\-based, decision\-driving narratives.
- Hold the full system in view across the organization, client, and market; shape decisions with long\-horizon thinking.
- Document modeling approaches, analytical pipelines, and best practices to support transparency and reuse.
- Contribute to RS21 business development, proposal efforts, and technical volume authorship as a credible senior voice.
Qualifications
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Required
- Master's degree (or Bachelor's with equivalent experience) in data science, statistics, computer science, or a related quantitative field.
- 7\+ years of hands\-on data science experience, with at least 3 years in a senior, lead, or principal\-level capacity.
- Prior experience directly managing data scientists, including hiring, performance management, and career development.
- Deep, hands\-on experience with statistical modeling, machine learning, and experimentation methodologies, applied to real\-world business problems.
- Proven ability to design, validate, and deploy production\-grade predictive and ML models, including rigorous evaluation and monitoring practices.
- Demonstrated experience with LLM, generative AI, or applied AI workflows, including prompt engineering, fine\-tuning, evaluation frameworks, or RAG\-based approaches.
- Hands\-on experience with AWS data science and ML services such as SageMaker, Bedrock, Redshift, Athena, Glue, and EMR.
- Track record of client\-facing work: requirements gathering, stakeholder communication, and translating business needs into rigorous analytical solutions.
- Experience functioning as a technical program lead owning delivery plans, managing Jira\-based project tracking, and coordinating cross\-functional technical teams.
- Strong statistical reasoning and systems thinking, able to hold the full picture while executing in the details.
- Excellent written and verbal communication skills with demonstrated ability to adapt technical depth to audience.
Preferred
- PhD in a quantitative discipline (statistics, applied math, computer science, economics, or related field).
- AWS certifications: Machine Learning – Specialty, Solutions Architect – Professional, or equivalent.
- Experience with MLOps tooling (MLflow, SageMaker Pipelines, or similar) and model monitoring frameworks.
- Background in consulting, professional services, or multi\-client delivery environments.
- Familiarity with causal inference, Bayesian methods, or advanced experimental design.
- Experience with Databricks, Spark, or distributed computing frameworks at production scale.
- Exposure to DoD, federal, or regulated\-sector data environments; FedRAMP\-compliant architecture experience a plus.
Salary Context
This $132K-$178K range is above the median 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 RS21: A Data Science and Visualization Company, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($155K) sits 20% below the category median. Disclosed range: $132K to $178K.
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.
RS21: A Data Science and Visualization Company AI Hiring
RS21: A Data Science and Visualization Company has 2 open AI roles right now. They're hiring across Data Scientist. Positions span Albuquerque, NM, US, US. Compensation range: $178K - $178K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 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.
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