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/GitHub) 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, NumPy, 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/GitHub) 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) concepts.
- Ability to communicate technical concepts clearly to diverse audiences.
- Strong collaboration and teamwork skills in a cross-functional environment.
- Attention to detail and organizational skills.
#LI-JP1
If you have a disability and need an accommodation in relation to the application and/or recruitment process, please email us at: [email protected].
WORKING CONDITIONS:
Our office is a modern space that fosters collaboration and creativity. Teams work closely together, sharing ideas and solutions in a supportive atmosphere. We provide all necessary equipment, including dual monitors and ergonomic chairs, to ensure a comfortable workspace.
DISCLAIMER:
The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to the job.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
Skills Required
- Bachelor's degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or related field and a minimum of 7 years experience; or Master's degree and a minimum of 5 years experience; or equivalent combination of education and experience
- Experience applying AI/ML methods to structured or unstructured data (years required vary by degree)
- Intermediate proficiency in Python (Pandas, NumPy, scikit-learn)
- 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 and 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)
- Familiarity with data pipeline concepts, version control (Git/GitHub), and CI/CD best practices
- 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)
- Ability to communicate technical concepts clearly to diverse audiences
- Strong collaboration and teamwork skills in a cross-functional environment
- Attention to detail and organizational skills
Exelixis Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Exelixis and has not been reviewed or approved by Exelixis.
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Retirement Support — Retirement plans are highlighted as a strength, featuring a 401(k) with generous company contributions and immediate vesting. This emphasis signals strong long‑term financial support within the total rewards package.
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Healthcare Strength — Healthcare coverage is positioned as comprehensive, with multiple medical plan options (HDHP with HSA, EPO, and Kaiser HMO in CA) and employer HSA contributions in 2026. Posted plan documents and contribution details indicate structured support for managing care and costs.
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Parental & Family Support — Family support is robust, including fully paid parental leave, caregiver leave, and expanded fertility benefits. Additional elements such as grandparent leave and backup care broaden assistance across life stages.
Exelixis Insights
What We Do
Every Exelixis employee is united in an ambitious cause: to launch innovative medicines that give patients and their families hope for the future. In this pursuit, we know our employees are our most valuable asset. After operating in the challenging biotech sector for more than 25 years, we have a proven track record of resiliency in the face of adversity. The success of our lead product has provided a solid commercial foundation allowing us to reinvigorate our research efforts, and grow our team in areas such as Drug Discovery, Clinical Development and Commercial. As we expand our global partnerships and further reinvest in R&D to help us discover the next breakthrough for difficult-to-treat cancers, we’re seeking to add talented, dedicated employees to power our mission. Cancer is our cause. Make it yours, too. Please see our Community Guidelines: bit.ly/2XXw9w3 For more information about Exelixis, please visit www.exelixis.com, follow @ExelixisInc on Twitter or like Exelixis, Inc. on Facebook.








