Data Science Engineer V - US

Rackspace Technology

3d•$165,830 - $243,142

About The Position

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. This role involves architecting and owning end-to-end delivery of complex statistical/ML and generative AI initiatives spanning multiple use cases, as well as complex Shiny applications and R-based analytical tools. The position also includes leading design reviews, setting technical direction, mentoring junior and mid-level data scientists, owning human-in-the-loop validation processes for high-stakes models, and owning bias/safety/explainability evaluation for AI outputs, with a focus on pediatric populations. The role requires partnering directly with Research PIs on study design, analysis planning, and interpretation of results, advising on the use of HPC/research-computing resources, and building scalable data science products. Collaboration with clinicians and business owners to define and prioritize high-impact problems is also a key function.

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.

Nice To Haves

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.

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.