Senior Data Scientist, Digital and Data Science – Analytical Sciences

Role Summary

As the Senior Data Scientist within Digital and Data Science for the Analytical Sciences department, you will lead the design, testing, and validation of digital solutions and data science applications that improve analytical methods and accelerate laboratory digitalization. You will manage key projects that drive operational and data automation, architect fit-for-purpose technologies to transform analytical workflows, and establish best practices across teams. Partnering with assay development scientists, you will define strategy and deliver advanced analytics to support process development, product characterization, and testing methodologies for novel engineered cell therapy products. You will collaborate with the Process Sciences, Analytics, and Technology (PSAT) team and the Quality organization within Cell Therapy Development & Operations (CDTO), building internal and external capabilities and mentoring junior team members. You will report to the Associate Director, Analytical Sciences, and can be performed from either Santa Monica, CA.

Responsibilities

• Lead end-to-end development of statistical and ML solutions for analytical method development, robustness, and lifecycle management (design, qualification, validation, transfer).
• Architect scalable data pipelines and analytics platforms integrating instrument data, LIMS/ELN, MES/historians, and cloud environments; ensure data integrity (ALCOA+) and traceability.
• Design and implement DoE, multivariate analysis, chemometrics, and advanced modeling (e.g., mixed effects, Bayesian, time-series, anomaly detection) to optimize assays and processes.
• Implement and govern MLOps/SDLC practices (versioning, CI/CD, model registry, monitoring) and champion code quality, testing, and documentation.
• Establish analytical dashboards and SPC/control charts for method performance.
• Lead lab digitalization projects, including instrument connectivity and data automation workflow orchestration, and template standardization.
• Evaluate, select, and manage vendors/partners for analytics, data platforms, and instrumentation integrations; drive successful technology transfers with third parties.
• Serve as a senior technical partner to PSAT and Quality within CDTO to align analytics solutions with regulatory expectations and operational needs.
• Translate scientific questions into analytical roadmaps, define success KPIs, and communicate results and risks to all stakeholders.
• Mentor and upskill scientists and data practitioners; conduct code and study design reviews; set standards for reproducible research.
• Lead computerized system validation/assurance (CSV/CSA) for analytics tools and models, incorporating 21 CFR Part 11, data integrity, and GxP considerations.
• Author and review validation plans/reports, SOPs, and technical documentation supporting regulatory interactions and audits.

Skills

• Statistical and ML expertise: strong foundation in experimental design (screening/optimization, response surface), regression and GLMs, mixed models, multivariate analysis, clustering, time-series, and Bayesian methods; proficiency with chemometrics for analytical data.
• Programming and platforms: Python or R; SQL; Git, Docker, and CI/CD; cloud analytics (AWS/Azure/GCP) and tools such as Databricks, Snowflake, MLflow/Kubeflow, Airflow/Prefect.
• Laboratory/analytical domain: experience with common analytical modalities and data structures (e.g., flow cytometry, imaging, plate readers, immunology assays, omics analysis) and integration with ELN/LIMS.
• Cross-functional leadership: ability to lead initiatives, influence without authority, and present complex analyses to senior stakeholders.
• GxP, data integrity, and validation documentation: working knowledge and experience authoring validation documentation.

Qualifications

• Seven (7) years with BS/BA, or five (5) years with MS/MA in data science, computational biology, bioengineering, or a related field with relevant post-graduate experience.
• Experience with DoE, regression, GLMs, mixed models, multivariate analysis, clustering, time-series, and Bayesian methods; chemometrics for analytical data.
• Experience with Python or R; SQL; Git, Docker, CI/CD; cloud analytics (AWS/Azure/GCP) and tools such as Databricks, Snowflake, MLflow/Kubeflow, Airflow/Prefect.
• Experience with analytical modalities and data structures (flow cytometry, imaging, plate readers, immunology assays, omics analysis) and ELN/LIMS integration.
• Ability to lead cross-functional initiatives, influence without authority, and present to senior stakeholders.
• Knowledge of GxP environments, data integrity, and 21 CFR Part 11; experience authoring validation documentation.

Education

• BS/BA in data science, computational biology, bioengineering, or related field; MS/MA preferred.