Exel

Exel

Senior AI Data Scientist I

Alameda, CA · Senior

Sponsorship not specified$144k-$203kDetected 15 days ago
PythonGitSQLDatabricksAWSCloud PlatformsCI/CDMachine Learningscikit-learnPandasNumPyData EngineeringData ScienceData VisualizationNLPLLMsStatisticsJiraBioinformaticsCollaborationPublic SpeakingGxPBiostatistics

About the role

  • 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.
  • The base pay range for this position is $143,500 - $203,000 annually.
  • The base pay range may take into account the candidate's geographic region, which will adjust the pay depending on the specific work location.

Responsibilities

  • 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.
  • 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.
  • Performs other duties as assigned

Requirements

  • A minimum of one (1) year of experience applying AI/ML methods to structured or unstructured data.
  • A minimum of three (3) years of experience applying AI/ML methods to structured or unstructured data.
  • A minimum of seven (7) years of relevant professional experience, including demonstrated application of AI/ML methods to structured or unstructured data.
  • 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.
  • Bachelor's degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field and a minimum of 7 years of experience
  • Master's degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field and a minimum of 5 years of experience
  • 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.
  • Intermediate proficiency in Python (Pandas, NumPy, scikit-learn) for data manipulation and model prototyping.

Skills

  • Equivalent combination of education and experience.
  • Knowledge, Skills and Abilities

Compensation

  • The base pay range for this position is $143,500 - $203,000 annually.
  • The base pay range may take into account the candidate's geographic region, which will adjust the pay depending on the specific work location.

Benefits

  • Intermediate understanding of supervised and unsupervised learning fundamentals, including model evaluation.
  • 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.
  • Create interactive dashboards and visualizations that support clinical data review, study-health monitoring, and decision-making across cross-functional stakeholders.

This listing is sourced directly from Exel's careers page and normalized into a canonical job model.