[Hiring] Data Science Engineer V @Rackspace Technology

Role Description

Owns complex, cross-cutting statistical/ML and generative AI initiatives that solve high-impact clinical and business problems, setting technical direction for the area, mentoring data scientists, and partnering directly with clinical and business leaders.

Qualifications

• 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.

Requirements

• 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; 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.

Job Functions

• 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.

Benefits

• Compensation range from $165,830.00/year in the lowest geographic market up to $243,141.80/year in the highest geographic market.
• Pay based on market location and job-related knowledge, skills, and experience.
• Incentive compensation opportunities in the form of annual bonus or incentives, equity awards, and an Employee Stock Purchase Plan (ESPP).