Principal Data Scientist

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.

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.

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.

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.

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.

Contribute to RS21 business development, proposal efforts, and technical volume authorship as a credible senior voice.

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.

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.