Clinical Data Scientist

N-Power Medicine•Redwood City, CA

21h•Remote

About The Position

N-Power Medicine is seeking a Clinical Data Scientist to own the final stage of its clinical data pipeline, transforming complex datasets from manual abstraction, AI models, and electronic sources into high-quality, analysis-ready deliverables for pharmaceutical partners. This role sits at the intersection of data science and clinical research, requiring expertise in data cleaning, transformation, and statistical programming (R, Python, SQL) to generate reports and ensure data integrity, accuracy, and regulatory compliance. The ideal candidate will collaborate cross-functionally with Clinical Operations, AI Engineers, and Data teams to validate outputs, automate workflows, and improve data infrastructure, while maintaining rigorous documentation and adhering to privacy standards. Candidates should be proficient in a Posit development environment and demonstrate familiarity with Github, and Jira. Experiences with Databricks and AI code development tools are preferred. This is an opportunity for a detail-oriented, technically versatile professional with 2–5+ years of experience in clinical data science or related fields to contribute to accelerating drug development in a mission-driven, fast-growing environment.

Requirements

Expertise in data cleaning, transformation, and statistical programming (R, Python, SQL).
Proficiency in a Posit development environment.
Familiarity with Github and Jira.
2–5+ years of experience in clinical data science or related fields.

Nice To Haves

Experiences with Databricks and AI code development tools.

Responsibilities

Own the final stage of its clinical data pipeline, transforming complex datasets from manual abstraction, AI models, and electronic sources into high-quality, analysis-ready deliverables for pharmaceutical partners.
Generate reports and ensure data integrity, accuracy, and regulatory compliance.
Collaborate cross-functionally with Clinical Operations, AI Engineers, and Data teams to validate outputs, automate workflows, and improve data infrastructure.
Maintain rigorous documentation and adhere to privacy standards.