Principal Data Scientist - Albuquerque, NM (Hybrid) (Albuquerque)

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Principal Data Scientist - Albuquerque, NM (Hybrid)

Salary Range: $132,000.00 To $178,000.00 Annually

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. This is a hybrid position based out of our Albuquerque office.

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

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.
• 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.
• 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.
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