Senior AI Data Scientist

Position: Senior AI Data Scientist I
## Senior AI Data Scientist IApplylocations:
Alameda, CAtime type:
Full time posted on:
Posted Todayjob requisition :
JR6959
** SUMMARY/JOB PURPOSE
** The Senior AI Data Scientist I develops, trains and validates AI/ML models and analytics solutions that transform complex clinical datasets into analysis-ready deliverables supporting drug-development decisions. Leveraging statistical programming (R, Python, SQL) and machine-learning techniques, this role executes automated workflows, data quality assurance, and regulatory-compliant outputs within a GxP-governed clinical data pipeline. This position exists to advance the organization's AI/ML and data science capabilities across clinical development - collaborating with Statistical Programming, Clinical Data Management, and Clinical Operations to accelerate data-driven insights, improve data infrastructure, and ensure the accuracy and reproducibility of analytical outputs that inform study-level and portfolio-level decisions.

** ESSENTIAL DUTIES/RESPONSIBILITIES
*** Build, train and validate machine-learning models (supervised and unsupervised) on clinical datasets under the direction of senior data scientists, ensuring model performance meets predefined acceptance criteria.
* Execute data cleaning, transformation, and standardization tasks across clinical datasets from EDC, vendor and real-world data sources, aligning outputs with CDISC (SDTM/ADaM) standards.
* Develop and maintain LLM-based and generative AI-workflows for automated TLF review and ad-hoc analytical queries, applying human-in-the-loop validation to ensure output reliability.
* Create interactive dashboards and visualizations that support clinical data review, study-health monitoring, and decision-making across cross-functional stakeholders.
* Execute data validation checks and quality-assurance procedures to ensure accuracy, reproducibility and compliance of analytical outputs with GxP requirements.
* Support the development and maintenance of data pipelines on Databricks and AWS cloud infrastructure, applying version control (Git/Git Hub) and CI/CD best practices.
* Collaborate with Statistical Programming, Clinical Data Management, and Clinical Operations to deliver AI/ML project milestones and address study-level data needs.
* Prepare and maintain documentation of model development, data transformation, and validation activities consistent with SOPs and work instructions.
* Drive external scientific visibility and publication objectives by contributing to manuscripts, conference presentations and white papers that showcase clinical AI/data science innovations.
* Pursue continuous professional development in emerging AI/ML techniques, cloud-based data platforms, and clinical data science methodologies to advance team capabilities.
* Performs other duties as assigned
* Complies with all policies and standards
** SUPERVISORY RESPONSIBILITIES
*** None
* * EDUCATION/EXPERIENCE/KNOWLEDGE/SKILLS & ABILITIES*
* ** Education
* ** Bachelor's degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field and a minimum of 7 years of experience; or,
* Master's degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field and a minimum of 5 years of experience; or,
* Equivalent combination of education and experience.
** Experience
* ** With PhD:
No prior experience applying AI/ML methods to structured or unstructured data.
* With Master's degree: A minimum of one (1) year of experience applying AI/ML methods to structured or unstructured data.
* With Bachelor's degree: A minimum of three (3) years of experience applying AI/ML methods to structured or unstructured data.
* Without degree: A minimum of seven (7) years of relevant professional experience, including demonstrated application of AI/ML methods to structured or unstructured data.
** Knowledge,

Skills and Abilities

*
* *
* Required:

*** Intermediate proficiency in Python (Pandas, Num Py, scikit-learn) for data manipulation and model prototyping.
* Intermediate proficiency in R for statistical analysis and visualization.
* Basic proficiency in SQL for data querying and transformation.
* Intermediate understanding of supervised and unsupervised learning fundamentals, including model evaluation.
* Basic familiarity with NLP, text mining and/or time series analysis techniques.
* Basic familiarity with LLM APIs and prompt engineering concepts.
* Basic knowledge of Databricks notebooks and Delta Lake concepts.
* Basic familiarity with AWS cloud services (S3, Lambda, Glue).
* Basic understanding of data pipeline concepts and data integration fundamentals.
* Intermediate proficiency with version control (Git/Git Hub) and project tracking tools (Jira).
* Intermediate proficiency with BI platforms including Spotfire, Tableau and/or Power BI.
* Basic understanding of the clinical development process and regulatory requirements (ICH, GxP).
* Basic familiarity with CDISC data standards (SDTM, ADaM)…