Data Science Engineer V - US

Rackspace Technology operates enterprise AI infrastructure, data, and software solutions for regulated and mission-critical environments. The Data Science Engineer V will own complex statistical, machine learning, and generative AI initiatives, architect R/Shiny analytical tools, set technical direction, mentor data scientists, and partner with clinical, research, and business leaders.

Responsibilities:

• Architect and own end-to-end delivery of complex statistical/ML and generative AI initiatives spanning multiple use cases
• Architect and own delivery of complex Shiny applications and R-based analytical tools spanning multiple use cases
• Lead design reviews and set technical direction for own area; mentor junior/mid data scientists
• Own human-in-the-loop validation processes for high-stakes models within own domain, partnering with clinical SMEs
• Own bias/safety/explainability evaluation for owned AI outputs, with particular attention to pediatric-population risk
• Partner directly with Research PIs on study design, analysis planning, and interpretation of complex results
• Advise on appropriate use of HPC/research-computing resources for compute-intensive modeling work
• Build tools/libraries that scale data science products across multiple teams
• Partner directly with clinicians and business owners to define and prioritize high-impact problems

Requirements:

• Bachelor's degree (or higher) in a STEM field, or equivalent experience
• 6–9 years of experience in data science, with significant project ownership
• 4–6 years hands-on building and productionizing ML/generative AI solutions
• 4–6 years hands-on experience with R, RStudio, and Shiny application development, including production-grade interactive tools
• 1+ year informally mentoring data scientists
• Advanced expertise in statistics, ML, and data mining; independently designs model architectures and evaluation frameworks
• Advanced expertise in R and RStudio; independently designs and architects production-grade Shiny applications
• Deep understanding of generative AI/LLM architecture; independently designs RAG/agentic solutions for high-stakes clinical/operational use cases
• Deep understanding of HPC/research-computing workflows; independently determines when compute-intensive work warrants HPC resources
• Independently designs bias/safety evaluation frameworks, with rigorous scrutiny for pediatric populations
• Mentors mid-level and junior data scientists on modeling, R/Shiny best practices, and responsible-AI practices
• Occasionally presents technical work to cross-functional groups of 5–10 people
• Deep experience with responsible-AI evaluation in a pediatric/clinical setting
• Experience with fine-tuning or advanced RAG/agentic architectures
• Experience running analyses in an HPC or research-computing environment for large-scale or computing intensive studies
• Established working relationships with Research PIs, including study-design or analysis-planning input