We are seeking a talented data scientist that is interested in applying quantitative analytical methods to improve global public health. Tuberculosis (TB) is the second leading cause of death from an infectious disease worldwide, and innovative tools and approaches are needed to improve the diagnosis and care of individuals with TB disease. The Data Scientist will work with research scientists, clinicians, public health practitioners and industry collaborators both internationally and domestically to support the collection and analysis of a wide range of data types, including clinical, biological and digital data (images and sound).

Current Major Projects:

• Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) – This National Institutes of Health (NIH) U01-funded project established a platform for identifying promising early stage diagnostics from a broad range of product developers and conducting field evaluations in TB endemic countries to advance their development and evaluate their accuracy. The project includes field sites in up to 10 countries (Uganda, South Africa, the Gambia, Zambia, Vietnam, India, Philippines, Peru, Brazil and Georgia). As part of this initiative, we collect clinical and demographic data, biological specimens for large ‘Omics data analyses, and digital data including radiologic images, cough sounds and lung sounds. The Data Scientist will support data management to integrate these data types, develop and maintain the storage of digital data, and develop and evaluate prediction models for TB disease. Skills of interest include data management, cyberinfrastructure/cloud-based storage, computational statistics (R, Python) and machine learning, including the development of deep learning and neural network models.

• Childhood ‘Omics’ and Mycobacterium tuberculosis-derived BiOsignatures (COMBO) for TB diagnosis in high HIV prevalence settings – This NIH R01-funded study seeks to identify novel biomarkers that can diagnose TB in children, utilizing retrospective and prospective cohorts in Uganda, South Africa, the Gambia, Peru and India. We will develop and validate combined host- and pathogen-based biosignatures using large ‘Omics-based datasets including proteomics and metabolomics, combined with clinical data. The Data Scientist will work with clinical and translational researchers and bioinformaticians, and utilize machine learning approaches to identify biomarker panels that can be translated into accurate, non-invasive point-of-care diagnostic and triage tests for TB in children. Skills of interest include integration of large data sets, and machine learning with large datasets and assessment of diagnostic accuracy.

• PROgression of Tuberculosis infECTion in young children with and without HIV (PROTECT). This is an NIH-funded study that seeks to identify biomarkers that will predict if a child with evidence of TB exposure or infection will progression to symptomatic TB disease. Cross-sectional and serial biological samples from children from Uganda, South Africa, the Gambia, and Vietnam will be analyzed using high-throughput ‘Omics approaches including transcriptomics, proteomics, and metabolomics. The Data Scientist will work with clinical and translational researchers and bioinformaticians to identify biomarkers and biosignatures that can predict progression to TB disease in children. Skills of interest include data integration of large data sets, statistics including time-series analyses, and machine learning.

To see the salary range for this position (we recommend that you make a note of the job code and use that to look up): TCS Non-Academic Titles Search (ucop.edu)

Please note: The compensation ranges listed online for roles not covered by a bargaining unit agreement are very wide, however a job offer will typically fall in the range of 80% - 120% of the established mid-point. An offer will take into consideration the experience of the final candidate AND the current salary level of individuals working at UCSF in a similar role.

For roles covered by a bargaining unit agreement, there will be specific rules about where a new hire would be placed on the range.

To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html

The Department of Pediatrics is one of the largest and most complex departments in the School of Medicine. The department is comprised of 19 divisions employing over 400 faculty, 83 fellows, 84 residents, 31 Post Docs and non faculty academics and 170 staff. The Department has annual clinical income from inpatient and outpatient activities totaling over $90 million and annual extramural funding (contracts and grants) exceeding $40 million. The department ranks 17th in the nation among Pediatric departments for funding from the National Institutes of Health.

The Department’s clinical enterprise is based at the 183-bed UCSF Benioff Children’s Hospital San Francisco, the 191-bed UCSF Benioff Children’s Hospital Oakland, Mount Zion, Zuckerberg San Francisco General, and at a number of outreach clinics throughout Northern California, providing the Department with an unsurpassed opportunity to meet the needs of infants, children, and young adults throughout the Bay Area. In 2019, UCSF Benioff Children’s Hospitals ranked in all 10 children’s specialties in U.S. News and World Report.

• Bachelor's degree in Statistics, Computer / Computational / Data Science, Computer Engineering or Domain Sciences or Public Health with computer / computational / data specialization or equivalent experience
• Minimum 3+ years of related experience
• Intermediate knowledge of data science methods. This includes experience with longitudinal data, or demonstrate a strong willingness to learn
• Data formats: Must be able to manipulate wide range of data including clinical, demographic, biological ‘Omics data, images, and sound-based data that is cross-sectional and longitudinal, or demonstrate a strong willingness to learn
• Machine Learning: Must be able to take a dataset, develop a preprocessing pipeline, determine the appropriate machine learning approach, train a model and assess accuracy including generation of receiver operating characteristic (ROC) curves and calculation of sensitivity and specificity. Should have experience with development of deep learning models. Must have demonstrated experience with real-world data science projects outside of course work
• Software: Must be able to use virtual environments and know how to work with containers (Docker) or demonstrate a strong willingness to learn. Must be able to code in R and/or python, with experience with deep learning suits, such as TensorFlow and/or Pytorch. Should be familiar with high performance computing (HPC) or demonstrate a strong willingness to learn
• Experience setting up cloud-based data storage with security and compliance requirements or demonstrate a strong willingness to learn
• Proven skills and experience in independently resolving broad computing / data science problems
• Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines
• Experience working in a complex computing / data / cyberinfrastructure environment encompassing all or some of the following: HPC, data science infrastructure and tools / software
• Proven ability to understand research computing / data / cyberinfrastructure needs, mapping use cases to requirements and how systems / software / infrastructure can support those needs and meet the requirements. Demonstrated ability to develop and implement such solutions
• Demonstrated experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators
• Demonstrated ability to contribute research and technical content to peer-reviewed manuscripts, conference abstracts and/or grant proposals
• Must be able to multi-task, work in a team, interact with clinicians, researchers and collaborators, communicate results (written, oral)
• Must be interested in enabling health science through working on collaborative data science projects
• Demonstrated effective communication and interpersonal skills. Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization and to external research and education audiences

• Master’s degree or PhD degree in Statistics, Computer / Computational / Data Science, Computer Engineering or Domain Sciences or Public Health with computer / computational / data specialization or equivalent experience
• Advanced knowledge of data science methods
• Experience working on international collaborations
• Experience working on health-related research projects
• Skills in project management
• Knowledge of medical terminology with a focus on the diagnosis of tuberculosis or other infectious diseases

The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It is the only campus in the 10-campus UC system dedicated exclusively to the health sciences. We bring together the world’s leading experts in nearly every area of health. We are home to five Nobel laureates who have advanced the understanding of cancer, neurodegenerative diseases, aging and stem cells.

UCSF is a diverse community made of people with many skills and talents. We seek candidates whose work experience or community service has prepared them to contribute to our commitment to professionalism, respect, integrity, diversity and excellence – also known as our PRIDE values.

In addition to our PRIDE values, UCSF is committed to equity – both in how we deliver care as well as our workforce. We are committed to building a broadly diverse community, nurturing a culture that is welcoming and supportive, and engaging diverse ideas for the provision of culturally competent education, discovery, and patient care. Additional information about UCSF is available at diversity.ucsf.edu

Join us to find a rewarding career contributing to improving healthcare worldwide.

The University of California San Francisco is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.

Campus

006104 CMPTL AND DATA SCI RSCH SPEC 3

Clinical Systems / IT Professionals, Research and Scientific

99 - Policy-Covered (No Bargaining Unit)

Career

100%

Fully Offsite

Days

8 Hours

Monday-Friday 8:00am-5:00pm