Sr. Data Scientist, Clinical Data Solutions | Onsite San Diego HQ

Job Title:

Data Scientist

Neurocrine Biosciences is seeking a Data Scientist to develop, deploy, and maintain scalable data pipelines, analytical data products, and automation solutions that enable clinical teams to design, run, monitor, and analyze clinical trials while optimizing for safety, efficacy, quality, and efficiency. This role works cross-functionally with medical and technical professionals in Clinical Development, Clinical Operations, Biometrics, and IT to transform complex clinical and operational data into useful outputs for exploration, decision-making, and early risk identification.

• Collaborate with Clinical Development, Clinical Operations, Biometrics, and medical teams to support the design, execution, monitoring, and reporting of clinical trials

• Develop and maintain data pipelines, analytical products, and dashboards that make clinical and operational data more accessible, consistent, reliable, and actionable

• Identify and build operational metrics, statistical signals, risk indicators, and decision-support tools that help stakeholders detect emerging site, study, vendor, country, or workflow risks early enough to intervene

• Translate complex clinical and operational questions into practical technical solutions, including data ingression, transformation, modeling, visualization, monitoring, and consumption through readily accessible tools and technologies

• Serve as a technical resource for statistical and numerical methods resource for the team, helping determine whether observed differences, trends, outliers, or anomalies are meaningful, actionable, or likely noise

• Apply programming, statistics, visualization, and analytical techniques to support clinical trial oversight, data quality review, operational performance monitoring, patient stratification, predictive modeling, and exploratory analysis

• Develop and enhance AI-enabled, biomarker, translational, and digital measurement solutions that improve clinical review, automation, risk detection, and decision support

• Provide technical guidance and support to team members, promoting best practices for data organization, code development, and analytical techniques for scalable solution design

• Build and maintain production solution stacks across controlled multiple environments using Git, pull requests, code review, CI/CD, release documentation, and change control practices

• Optimize analytical solutions and cloud-based infrastructure to improve performance, reliability, usability, security, scalability, and cost efficiency

• Maintain enhancement backlogs, technical documentation, data definitions, business rules, validation evidence, and solution roadmaps to support deployed products and future improvements

• Bachelor's degree in Data Science, Computer Science, Engineering, Bio/Pharma Development, or related field and 4+ years in an analytical capacity supporting development and maintenance of solutions OR

• Master's degree in Data Science, Computer Science, Engineering, Bio/Pharma Development, or related field and 2+ years as noted above OR

• PhD or equivalent in Data Science, Computer Science, Engineering, Bio/Pharma Development, or related field and applicable academic or applied experience as noted above

• Demonstrated passion for practically solving complex clinical, operational, and technical problems with scalable technology

• Prior bio/pharma, CRO, medical device, healthcare technology, or similar regulated industry experience highly preferred

• Advanced proficiency in Python, R, or other mainstream programming languages

• Proficiency in SQL, relational database design and data modeling

• Proficiency with data platform products such as Databricks/Snowflake

• Proficiency with data exploration and visualization tools such as Tableau, Power BI, R, Shiny, Streamlit, or similar frameworks

• Strong practical understanding of statistics, including hypothesis testing, confidence intervals, regression, correlation, trend analysis, outlier detection, variance, and distinguishing meaningful signal from noise

• Experience with statistical analysis software and tools such as SAS, R, JMP, SPSS, Python, or similar platforms

• Experience with AI/ML, generative AI, NLP, model evaluation, human review, and responsible AI practices

• Experience with clean code principles, testing methodologies, Git/Git Hub, branching, pull requests, code review, versioning, and collaborative development workflows

• Experience with cloud infrastructure, cloud computing platforms, infrastructure-as-code concepts, CI/CD, environment management, monitoring, logging, and scalable deployment patterns

• Experience with CDISC standards, clinical data flows, GCP regulatory guidelines, and regulated clinical development processes preferred

• Experience with machine learning, AI, predictive modeling, patient stratification, operational risk modeling, and responsible use of AI in clinical data analysis

• Familiarity with clinical data capture, trial management,…