Assoc Director, Biostatistics

Gilead Sciences-posted 4 months ago

$177,905 - $230,230/Yr

Full-time • Senior

5,001-10,000 employees

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As a Biostatistics/Data Science leader, you will design, develop, and implement statistical workflows and solutions that support drug development, and other key business use cases across Gilead. Your work will emphasize building efficient, scalable, and compliant, statistical frameworks, data pipelines, automated processes, and reproducible R-based tools. You will oversee version control and collaborative coding workflows on GitHub, ensuring rigorous software engineering standards, robust code quality, and seamless integration into existing enterprise data infrastructures. You will be instrumental in leveraging open-source software development best practices within a pharmaceutical context, including familiarity with Pharmaverse packages and the unique challenges of regulated environments. Additionally, you will partner with internal stakeholders and IT to ensure alignment with infrastructure requirements, maintain data governance and compliance standards, and foster a culture of data-driven efficiency and continuous improvement. A successful candidate will be a strong technical project manager and a hands-on statistics/data science leader, comfortable overseeing complex R package initiatives, coordinating cross-functional teams, and ensuring high standards of reproducibility, transparency, and scalability.

Collaborate with statisticians on complex methodological issues, ensuring appropriate statistical approaches are selected, adapted, and implemented for clinical trial settings.
Translate advanced statistical concepts into robust, reproducible R code that can be applied to study data, including simulation studies, innovative trial designs, and novel estimation techniques.
Serve as a statistics/data science leader, shaping department-level standards, tools, and methodologies for data-intensive projects.
Collaborate closely with study teams, engineers, product managers, and other stakeholders to identify and implement efficient data workflows that accelerate R&D and other business operations.
Provide thought leadership in defining short- and long-range technical roadmaps for statistics and data science initiatives, ensuring alignment with organizational strategies.
Effectively manage code repositories, workflows, pull requests, and CI/CD pipelines to maintain code integrity and streamline development processes.
Act as the primary technical project manager for complex R package development tasks, ensuring timely delivery, version control, testing, and documentation.
Define and guide solution architectures for data workflows, integrating R package development and open-source tools into enterprise data pipelines.
Work with technical teams and business partners to identify and implement cloud, database, and infrastructure components that support large-scale, secure, and compliant solutions.
Drive continuous improvement in data modeling, automation, data formatting, and performance optimization, applying pharmaceutical industry best practices and regulatory considerations.
Develop and validate statistical software implementations of methodologies used in clinical trials, ensuring alignment with regulatory expectations and reproducibility standards.
Oversee the design, deployment, and maintenance of data workflows that emphasize automation, reproducibility, and consistent standards.
Lead the development and maintenance of custom R packages, ensuring adherence to industry standards and leveraging tools from the Pharmaverse ecosystem.
Champion modern, high-performance storage formats, cloud-based architectures, and distributed computing technologies to manage large datasets effectively in a regulated environment.
Implement strategies for efficient data pipelines, focusing on data ingestion, cleaning, transformation, validation, and quality control.
Ensure all data engineering deliverables, including R packages and automated workflows, adhere to best practices, corporate policies, and relevant regulatory requirements.
Identify cross-project synergies and opportunities for standardization, developing reusable code components and frameworks that align with Pharma open-source initiatives.
Manage resource planning and investments to support the execution of the data engineering strategy and ongoing initiatives.

Bachelor’s Degree and 10 years’ experience OR Master’s Degree and 8 years’ experience OR PhD and 2 years’ experience.
Experience consulting with statisticians on complex methodological issues and translating statistical theory into practical, validated software solutions.
Proven ability to implement statistical methods (e.g., adaptive designs, Bayesian methods, survival models, multiplicity adjustments) in clinical trial workflows using R.
Extensive experience with R programming and developing complex R packages, including testing, documentation, and dependency management.
Demonstrated experience using GitHub for version control, issue tracking, code reviews, and CI/CD to ensure high-quality, maintainable codebases.
Strong background in data engineering practices, pipeline development, data cleaning, data validation, and efficient ETL processes.
Experience with Pharmaverse packages and familiarity with open-source development workflows for clinical trials.

Proficiency in additional programming languages (e.g., Python, SQL, html, javascript) and distributed computing frameworks.
Experience building and managing AWS-based data pipelines for R.

Company-sponsored medical, dental, vision, and life insurance plans.
Discretionary annual bonus.
Discretionary stock-based long-term incentives.
Paid time off.

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