Gilead is looking to solve business and technical problems by improving descriptive statistics, data engineering, model building and evaluation, prediction, visualization, and automation for business use through advanced data science solutions.
Requirements
- 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., Python, SQL etc., with proven capabilities to manipulate large and sophisticated datasets using distributed computing technologies.
- Significant experience engineering and architecting data lakes, data warehouses and big data storage and platforms on AWS.
- Significant experience working with modern high-performance columnar storage formats.
- Strong proficiencies in problem-solving, algorithm design and complexity analysis.
- Strong knowledge of modern Machine Learning architectures, platforms and backend systems. Proven ability to develop highly complex Machine Learning models for automated data analysis.
Responsibilities
- Plays a key role in the ongoing continuous improvement of descriptive statistics, data engineering, model building and evaluation, prediction, visualization and automation for business use.
- 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.
- Uses Machine Learning best practices to ensure a high standard of quality for all deliverables.
- Defines and designs the overall data science solution architecture for assigned groups or projects.
- Coordinates infrastructure requirements with the business and IT to support the data science solution architecture.
Other
- Plays a lead role in development of team and department-level standards, tools and templates.
- Provides matrix leadership to small teams in the development and maintenance of data science processes and applications.
- Collaborates with scientists, engineers, product managers and other business stakeholders to design and implement software solutions.
- Creates and manages resource plans for assigned work. Identifies cross-project synergies to leverage efficiencies and ensure consistencies where appropriate.
- Ensures assigned work complies with established practices, policies and processes and any regulatory or other requirements.
