Data Science Engineer V

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.

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 product ionizing 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.

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.

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.

The compensation range for this position ranges from $/year in our lowest geographic market up to  USD/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job‑related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP).

Rackspace Technology is the trusted operator of the full enterprise AI stack, from governed private cloud to AI inference and agents in production. From edge to core to cloud, we operate the infrastructure, data layer, and software integration to deliver…