Senior AI Data Scientist I

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) 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.
  
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