Data Science Engineer V
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Job Description
Data Science Engineer V leading clinical ML, generative AI, and Shiny solutions. Rackspace Technology operates enterprise AI infrastructure and software for regulated, mission-critical environments.
Responsibilities:
- Own complex, cross-cutting statistical/ML and generative AI initiatives addressing high-impact clinical and business problems
- Set technical direction for the area and lead design reviews
- Mentor junior and mid-level data scientists on modeling, R/Shiny best practices, and responsible-AI practices
- Architect and own end-to-end delivery of complex statistical/ML and generative AI initiatives
- Architect and own delivery of complex Shiny applications and R-based analytical tools
- Own human-in-the-loop validation processes for high-stakes models in partnership with clinical SMEs
- Own bias, safety, and explainability evaluations for AI outputs, with particular attention to pediatric-population risk
- Partner with Research PIs on study design, analysis planning, and interpretation of complex results
- Advise on HPC/research-computing resources for compute-intensive modeling work
- Build tools and libraries that scale data science products across multiple teams
- Partner 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 of hands-on experience building and productionizing ML/generative AI solutions
- 4–6 years of hands-on experience with R, RStudio, and Shiny application development, including production-grade interactive tools
- 1+ year informally mentoring data scientists
- 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
- Advanced expertise in statistics, ML, and data mining
- Advanced expertise in R and RStudio
- Deep understanding of generative AI/LLM architecture
- Deep understanding of HPC/research-computing workflows
- Ability to independently design bias/safety evaluation frameworks for pediatric populations
- Ability to mentor mid-level and junior data scientists
- Ability to present technical work to cross-functional groups of 5–10 people
Benefits:
- Annual bonus or incentives
- Equity awards
- Employee Stock Purchase Plan (ESPP)
- Benefits offered by Rackspace Technology
- Equal employment opportunity and disability/special-need accommodation
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