University of California San Francisco-posted 5 days ago
Full-time • Mid Level
San Francisco, CA
5,001-10,000 employees

The Senior Data Scientist uses professional data science (including AI and biomedicine) concepts. He/she applies computational procedures to resolve a variety of analysis and research issues. Works on assignments of moderate to extended scope where analysis of data requires a review of a variety of factors. This specialist develops additional analyses as needed to achieve research objectives. The Computational Data Science Research Specialist will develop innovative approaches to apply AI/ML methods to clinical and biological data sets, and advise others on of effective approaches. This specialist may be required to utilize the elasticity of the AWS Cloud for Big Data Intensive (e.g. Hadoop/Spark) compute infrastructure and parallel system environment. This specialist assists in creating pipelines and configurations on a Linux-based distributed file system for Very Large health data, premise-hosted as well as public cloud based. Such data will be clinical, genotypic, biological scale, phenotypic and population level data of several categories, structured, semi-structured and unstructured data. Non-structured data includes genetic, text, images, and “messy” alphanumeric data. AWS and Linux system skills will help to build scalable general-purpose computational and inferential software tools to work with the data. The Senior Data Scientist will work with current and prospective partners via UCSF Business Development functions, like Center for Real-World Evidence ( CRWE), Office of Innovation, Technology & Alliances (ITA), to provide data-based analyses and consulting support to assist in developing and fulfilling partnerships. Typical partners are research labs, biotech or tech companies. The specialist will participate in working discussions with partners, as well as with UCSF business functions in support of the above goals; and will also evaluate third party tools, especially for Next Generation Sequencing, Foundation Models, Natural Language Processing (NLP), text mining and information retrieval, for adaptation into and use in our system, under guidance from the Data Science manager. The candidate will work towards ensuring system compliance with the university’s policies with respect to privacy and security. The final salary and offer components are subject to additional approvals based on UC policy. Your placement within the salary range is dependent on a number of factors including your work experience and internal equity within this position classification at UCSF. For positions that are represented by a labor union, placement within the salary range will be guided by the rules in the collective bargaining agreement. The salary range for this position is $97,100 - $145,700 (Annual Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html

  • Bachelor’s degree in Computer / Computational / Data Science, or biomedical computation, or Domain Sciences with computer / computational / data specialization or related area and minimum 3 years of relevant experience or an equivalent combination of education and experience.
  • Intermediate knowledge of large dataset computing/querying , especially Python/R, SQL (including access of “NoSQL” data stores), UNIX shell programming
  • Experience of managing projects of moderate scope and complexity
  • Strong knowledge of data science and AI at large scale, including biomedical data sciences, workflows and methods.
  • Experience of applying machine learning, statistical or similar data science techniques to real-world data
  • Intermediate knowledge of large dataset computing/querying , especially Python/R, SQL (including access of “NoSQL” data stores), UNIX shell programming.
  • 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, especially to a biomedical audience.
  • Proven skills and experience in independently resolving broad computing / data problems using introductory and / or intermediate principles.
  • Self -motivated, works independently or as part of a team, able to learn quickly, meet deadlines and demonstrate problem solving skills.
  • Demonstrated experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators.
  • Bachelor’s or Master’s degree in Computer Science, with specialty courses on Data Sciences and AI
  • 5+ years of relevant experience or an equivalent combination of education and experience
  • AWS, Unix/Linux OS and shell scripting, Python, Java, Rstudio, Jupyter, Hadoop, Spark, Hiv
  • Experience analyzing data on the order of tens of billions of records.
  • Fundamental knowledge of computer science, architectures and algorithmic optimization
  • Experience with applications programming, web development and data structures.
  • Demonstrated ability to regularly interface with management.
  • Experience of managing projects of moderate scope and complexity
  • Proven ability to successfully work on multiple concurrent projects.
  • Thorough experience working in a complex computing / data / CI environment encompassing all or some of the following: HPC including Cloud- and premise-based deployments, data science infrastructure and tools / software, and diverse domain science application base
  • Advanced skills associated with software design, modification, implementation and deployment, including object-oriented programming concepts.
  • Intermediate knowledge of secure software development.
  • Proven ability to understand functional and research computing / data needs, mapping use cases to requirements and how systems / software / infrastructure can support those needs and meet the requirements.
  • Demonstrated ability to develop conversion and system implementation plans and implement such solution.
  • Ability to contribute technical narrative to grant proposals.
  • Knowledge of or experience in clinical practice.
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