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Job Description

The Quality Governance, Risk and Analytics team within R&D’s Quality organization is growing and in search of a Senior Manager of Quality Data Science. This new position will play a pivotal and visible role supporting quality activities in drug development/clinical research and other business use cases across Gilead. This role will deliver through data engineering and visualizing for analytical, scientific, and other business exploration and decision-making, including deep learning, data automation, and defining and aligning cloud, database and other technical infrastructure requirements.

The Sr. Manager will partner with the Quality Data Analytics team to establish Quality Performance/Quality Health models to support the maturity of the Quality Management System (i.e., signal detection, risk analytics to support early detection and mitigation measures and continuous improvement). The Sr. Manager will work directly with Quality teams, to understand needs and challenges, and unleash the power of data and technology to provide solutions with measurable impact. The role will be a key thought partner and work closely with partner business areas to define and support the technology roadmap and next generation data analytics and automation.

PRIMARY RESPONSIBILITIES

Predictive Analytics & Models

• Support the creation and execution of new mechanisms to define, measure, and monitor quality performance/quality health of the QMS and other areas of quality operations (e.g. Audit, Clinical Quality, etc.).

• Designs data science workflows for the assigned area, incorporating best practice statistical analysis, data engineering, automation and visualization, deep learning and other contemporary machine learning techniques, models and tools.

• Builds, implements, tests, deploys and maintains innovative data and Machine Learning solutions to improve clinical trials and other business operations.

• Performs statistical analysis of results to tune / refine Machine Learning models.

• Collaborates with scientists, engineers, product managers and other business stakeholders to design and implement software solutions.

• Uses Machine Learning best practices to ensure a high standard of quality for all deliverables.

• Support the build of trending and modelling capabilities to analyze complex data, build insights and drive innovative process improvements of the QMS.

• Perform diagnostic, descriptive, predictive and prescriptive analytics to identify performance trends, root cause assessments and identify areas for continuous improvement.

• Work with complex, structured and unstructured data from a variety of sources and implement machine learning/algorithms to enable signal detection, trends, and identification of potential areas of risk.

• Assist in the development of mechanisms to translate quality health of the portfolio/studies through utilizing outputs from descriptive analytics such as those from Tableau or Spotfire, and advanced analytics including simulation, risk modeling, and machine learning.

• Provide direct support on data analytics and automation to contribute to quarterly deliverables related to management reviews and performance analytics.

• Support the build of a span of tools and libraries to streamline the data analytics automation efforts and knowledge sharing.

• Collaborate with infrastructure teams to help design and create a data platform within Gilead’s AWS data lake.

Strategy and Stakeholders

• Provide guidance and support on statistical/analytical methodologies to recommend and implement modeling techniques to support defined business cases and needs.

• Interface with internal and external stakeholders, including presenting at appropriate engagements, building visibility into R&D Quality’s strategy and impact in the Quality Performance/Data Analytics space.

• Support the development and training of internal staff, including fostering a commitment to quality in individuals and a culture of quality within the organization.

BASIC QUALIFICATIONS

• PhD in computer science, computer engineering, information systems or related discipline with some general business knowledge and work experience. PhD with 4+ years’ data science experience is preferred.

• MS in computer science or related discipline with 5+ years’ data science experience

• BS in computer science or related discipline with 7+ years’ relevant experience in data science

• Significant experience in end-to-end data science techniques, including data engineering, statistical analysis, modeling, visualization, presentation and warehousing

• Significant experience building Machine Learning models and libraries

• Strong programming skills in key languages used by Data Science, e.g., JAVA, Python, SAS, SQL, SCALA, etc., with proven capabilities to manipulate large and sophisticated datasets using distributed computing technologies.

• Experience contributing to open source projects

• A track record in publications within own field is preferred

KNOWLEDGE and OTHER REQUIREMENTS

• Has complete knowledge in two or more key areas, such as statistical analysis, data automation, data mining and/or data visualization

• Has advanced knowledge of data science best practices and tools and has a track record of applying this to significantly improve decision-making effectiveness and efficiencies

• Demonstrated ability to be flexible and adaptable to change, to move between projects easily and provide support/expertise where needed

• Proven analytical abilities with high attention-to-detail as demonstrated through past experiences and/or academic achievements

• Strong knowledge of software development methodologies and tools

• Strong knowledge of modern Machine Learning architectures, platforms and backend systems. Proven ability to develop highly complex Machine Learning models for automated data analysis.

• Ability to develop complex Machine Learning models for automated data analysis (e.g., hypothesis testing, correlation analysis, regression analysis, Bayesian models, neural networks, and predictive modeling)

• Demonstrated mindset of commit early and often, metrics before models, and shipping high-quality production code

The salary range for this position is: $136,000.00 - $176,000.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans*.

For additional benefits information, visit:

https://www.gilead.com/careers/compensation-benefits-and-wellbeing

* Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

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For Current Gilead Employees and Contractors:

Please log onto your Internal Career Site to apply for this job.

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