Sr Analyst, Data Scientist

Gilead Sciences•Foster City, CA

8d•$117,895 - $152,570

About The Position

We are seeking a talented and highly motivated Data Scientist for our Advanced Analytical Technologies team within the Analytical Development organization. The Data Scientist will research, develop, and operationalize advanced machine learning and statistical solutions that improve analytical development across Pharmaceutical Development and Manufacturing. This role will translate scientific and business challenges into scalable, data-driven applications spanning quantitative analytics, predictive modeling, computer vision, and other advanced computational methods. The individual will collaborate with lab scientists, data scientists, engineers, and project managers to deliver robust solutions, promote machine learning best practices, and strengthen a culture of data-driven decision-making.

Requirements

Bachelor’s degree with at least 4 years of relevant experience in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Chemical Engineering, or a related discipline.
OR Master’s degree with at least 2 years of experience in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Chemical Engineering, or a related discipline.
Strong Python programming skills, including experience developing maintainable, reusable, documented, and well-tested analytical or machine learning code.
Advanced statistical and computational modeling expertise, with the ability to select, develop, validate, interpret, and improve models for complex problems.
Advanced proficiency with Python-based machine learning frameworks and libraries.
Experience with computer vision, including classical image-processing methods and deep learning approaches.
Knowledge of data modeling, architecture, pipelines, and infrastructure required to deploy and operationalize machine learning solutions.
Strong written, verbal, and visual communication skills, particularly when explaining model performance, limitations, findings, and recommendations.
Demonstrated collaboration, problem-solving, and project execution skills within multidisciplinary environments.

Nice To Haves

Experience applying data science or machine learning in pharmaceutical development, manufacturing, analytical sciences, or another regulated scientific environment is preferred.

Responsibilities

Research and develop machine learning algorithms across quantitative analytics, computer vision, predictive analytics, and advanced statistical modeling to improve pharmaceutical development and manufacturing processes.
Manage the full machine learning model lifecycle, including requirements gathering, exploratory data analysis, visualization, model development, validation, deployment, monitoring, and continuous improvement.
Support end-to-end algorithm and application development, including software package development, compute environment configuration, model architecture, code reviews, testing, documentation, and operationalization.
Translate scientific and business challenges into practical data science solutions with measurable technical, scientific, and operational outcomes.
Apply best practices in machine learning, software engineering, reproducibility, and model governance to ensure deliverables are reliable, scalable, maintainable, and high quality.
Collaborate with multidisciplinary technical teams, including data science architects, data engineers, analysts, project managers, laboratory scientists, and application developers.
Partner with stakeholders in an agile environment to scope, prioritize, plan, design, and execute artificial intelligence and machine learning projects.
Work cross-functionally with analytical development, manufacturing, IT, and technical development teams to advance AI-driven applications and improve scientific and business outcomes.
Promote a data-driven culture by encouraging quantitative decision-making, supporting well-designed experiments, identifying organizational capabilities and needs, and advancing responsible AI adoption.
Communicate complex analyses and model results through clear reports, visualizations, presentations, and recommendations tailored to scientific and cross-functional audiences.