Data Science Engineer V

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of intricate, cross-disciplinary statistical/ML and generative AI projects that tackle significant clinical and business challenges.

• Establish the technical vision for the domain and oversee design reviews.

• Guide junior and mid-level data scientists on modeling, R/Shiny best practices, and responsible AI methodologies.

• Design and manage the complete delivery of complex statistical/ML and generative AI projects.

• Create and oversee the development of sophisticated Shiny applications and R-based analytical tools.

• Manage human-in-the-loop validation processes for critical models in collaboration with clinical subject matter experts (SMEs).

• Handle evaluations of bias, safety, and explainability for AI outputs, especially regarding risks in pediatric populations.

• Collaborate with Research Principal Investigators (PIs) on study design, analysis planning, and interpretation of complex findings.

• Provide guidance on high-performance computing (HPC) and research computing resources for compute-intensive modeling tasks.

• Develop tools and libraries that enhance data science products across various teams.

• Work alongside clinicians and business stakeholders to identify and prioritize significant challenges.

⛳️ Requirements

• Bachelor's degree (or higher) in a STEM discipline, or equivalent experience.

• 6–9 years of experience in data science with substantial project ownership.

• 4–6 years of practical experience in developing and implementing ML/generative AI solutions.

• 4–6 years of hands-on experience with R, RStudio, and Shiny application development, including production-grade interactive tools.

• More than 1 year of informal mentorship for data scientists.

• Extensive experience with responsible AI evaluation in pediatric or clinical settings.

• Proficiency in fine-tuning or advanced RAG/agentic architectures.

• Experience conducting analyses in an HPC or research computing environment for large-scale or computationally intensive studies.

• Established relationships with Research PIs, including contributions to study design or analysis planning.

• Advanced knowledge in statistics, ML, and data mining.

• Advanced proficiency in R and RStudio.

• Comprehensive understanding of generative AI/LLM architecture.

• In-depth knowledge of HPC/research computing workflows.

• Capability to independently design bias and safety evaluation frameworks for pediatric populations.

• Ability to mentor mid-level and junior data scientists.

• Skill in presenting technical work to cross-functional groups of 5–10 individuals.

🏝️ Benefits

• Annual bonus or incentives.

• Equity awards.

• Employee Stock Purchase Plan (ESPP).

• Benefits provided by Rackspace Technology.

• Equal employment opportunity and accommodations for disabilities or special needs.