Associate Scientist - Bioinformatics

POSITION OVERVIEW:

Clinical Bioinformatics & Exploratory Analytics supports drug development and other business use cases across Gilead by providing data analytical, engineering, and visualization solutions to enable scientific and other business exploration and decision-making, including statistical analysis, genomic data processing and analysis, general machine learning and deep learning, data automation, and building on-premise/cloud computing, data organization, and other technical infrastructure. You will lead design, development and implementation of statistical, bioinformatics and machine learning methods that integrate large multi-modal datasets to derive quantitative biological insights that support clinical development, translation of clinical findings and other business decision-making. You will develop and utilize a comprehensive understanding of biological and related data and foster learning using data engineering, automation and visualization tools and applications to broaden efficiency with data accessibility, while enabling a culture of data-driven decision-making. You may also contribute to presentations and publications of clinical genomics and biomarker findings either as conference publications, journal publications or regulatory documents.

EXAMPLE RESPONSIBILITIES:

• Plays a key role in both internal and external collaborations in complex data analysis, including but not limited to genomic data, protein assay data, cytokine and chemokine data from patient samples, to support patient stratification and biomarker selection for Gilead’s clinical development programs.
• Has a strong background in HIV and microbiome biology and hands on experience with analyses of related biomarker and omics datasets as described above.
• Develops predictive models using statistical techniques and machine learning (e.g. logistic regression, random forest, etc.) for analysis of large omics and high dimensional data from internal, publicly available, commercial and Real-World datasets to enable target identification / assessment, drug combinations, understanding of MOA, disease mechanisms, etc.
• Designs and executes data analysis plans, communicates findings and recommends follow-up actions in multiple settings, including one-to-ones, seminars, group and project meetings.
• Develops and implements quality assessments, statistical analysis and data visualization for multi-omics datasets, e.g., RNA-Seq, single cell sequencing, WES, WGS, etc.
• Develops data dashboards, visualizations and analytical tools to improve visualization, integration, and accessibility of complex clinical data and enable data analysis and exploration by the broader Research & Development organization.
• Provides expertise and technical consultation for external collaborations/partnerships in academia and industry.
• Identifies and recommends continuous improvement opportunities that advanced computational methods can address for biomedical data lifecycle management, data integration, data security, data quality management and metadata management.
• Ensures assigned work complies with established practices, policies and processes and any regulatory or other requirements.

REQUIREMENTS:

We are all different, yet we all use our unique contributions to serve patients. Please see the following for the qualifications and skills we seek for this role.

Minimum Education & Experience

• MS in statistics, mathematics, bioinformatics, genomics, computer science or related discipline with 3+ years’ bioinformatics or related experience.
• BS in statistics, mathematics, bioinformatics, genomics, computer science or related discipline with 5+ years’ relevant experience in bioinformatics or related field.
• Significant experience working with multi-omics and large-scale biological datasets.
• Significant experience working with a broad range of statistical methods, including machine learning methods.   
• Strong programming skills in key languages used by Bioinformatics and Statistics, e.g., R, Python, etc., with proven capabilities to manipulate large and sophisticated datasets.
• Significant experience working with cloud computing systems, data management systems, advanced statistical software and visualization tools.
• Strong proficiencies in problem-solving, bioinformatics or other data analytics algorithm design, genomic and proteomic technologies, and high throughput experimental techniques, e.g., next generation sequencing technologies, single cell technologies, and/or their applications.
• A track record in publications within own field is preferred.

Knowledge & Other Requirements

• Demonstrated ability to be a fast learner.
• 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, including statistical knowledge, such as probability theory, statistical power, univariate and/or multivariate analysis, unsupervised and supervised analysis, regression analysis, survival analysis, etc.
• Proven ability to conduct effective and efficient exploratory analysis on large volumes of data and identify key descriptive and inferential properties.
• Strong communication and organizational skills.