Senior Expert Data Science

Novartis•East Hanover, NJ

12d•$138,600 - $257,400•Hybrid

About The Position

Turn complex scientific data into insights that help advance the next generation of cell and gene therapies. As a Senior Expert Data Science within the Data and Statistical Sciences team, you will partner with development scientists, process engineers and cross-functional teams to strengthen process and assay understanding. You will apply advanced analytics, build reusable data solutions and communicate clear evidence that supports Chemistry, Manufacturing and Controls decision-making across technical research and development.

Requirements

Bachelor's degree with seven or more years, master's degree with five or more years, or doctorate with two or more years of relevant experience.
Experience applying data science, statistics, machine learning or advanced analytics to complex scientific, engineering or manufacturing problems.
Proficiency with either Python or R.
Knowledge of regression, hypothesis testing, experimental design, multivariate analysis and predictive modelling.
Ability to translate ambiguous scientific or business questions into structured, reproducible and well-documented analyses.
Clear communication and effective collaboration with technical and non-technical stakeholders in a cross-functional environment.

Nice To Haves

Knowledge of bioprocess engineering, Chemistry, Manufacturing and Controls development, pharmaceutical manufacturing or analytical development.
Experience creating data pipelines, interactive dashboards or analytical applications in a regulated biopharmaceutical environment.
Experience with process monitoring, process characterization, assay analytics, comparability assessments, control strategy support, or manufacturing investigation analytics.
Experience with data tools commonly used in biopharmaceutical development and manufacturing, such as JMP/JSL, SAS, or electronic laboratory/manufacturing data systems.
Ability to influence without authority, drive alignment across stakeholders, and deliver practical analytical solutions in complex organizational settings.

Responsibilities

Partner with scientific and cross-functional teams to frame critical manufacturing, assay and product-understanding questions.
Apply statistical, machine learning and multivariate methods to complex bioprocess, analytical and experimental datasets.
Deliver robust analyses supporting process characterisation, monitoring, comparability, investigations and control strategy development.
Build pipelines that ingest, clean, transform and integrate data from multiple sources.
Develop curated datasets, models and analytical products for repeatable analysis, visualisation and reporting.
Create clear dashboards, reports and presentations for technical and non-technical audiences.
Lead data science projects from problem definition through delivery, managing scope, milestones, risks and stakeholders.
Guide colleagues on reproducible analytical practices while contributing to an inclusive, scientifically rigorous team culture.