Staff Data Scientist
University of Chicago
Department
BSD IPP - Machine Learning
About the Department
Job Summary
The position also involves partnering with lab members and collaborators to scope and launch new projects, disseminating findings through conferences and journals, and contributing to grant proposals and reports. This is an opportunity to play a key role in advancing cutting-edge computational approaches in immunology while supporting the broader mission of the lab.
Responsibilities
Assist in the research, development, and/or application of machine learning models to immunology, working with students, postdocs, and other lab members on model design and implementation.
Plan and run experiments; collect, curate, annotate, and analyze data; maintain high-quality training/validation datasets with robust metadata.
Establish and enforce lab-wide standards for coding, data management, and documentation; build rigorous benchmarking frameworks, define metrics, and deliver interpretation reports.
Mentor trainees and research staff through code reviews, technical support, and modeling best practices in software engineering and computational immunology.
Collaborate across the lab and with partners to scope and launch new research projects.
Disseminate results via journal publications and presentations at conferences, seminars, and lab meetings; conduct literature reviews and prepare materials for research talks.
Contribute to grant proposals and reports; co-author manuscripts; provide peer review for manuscripts and grant applications.
Participate in general lab operations and perform other related duties as needed.
Analyzes moderately complex data sets for the purpose of extracting and purposefully using applicable information.
Provides professional support to staff or faculty members in defining the project and applying principals of data science in manipulation, statistical applications, programming, analysis and modeling.
Performs other related work as needed.
Minimum Qualifications
Education:
Minimum requirements include a college or university degree in related field.
Work Experience:
Certifications:
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Preferred Qualifications
Education:
PhD or Master’s degree in Computational Biology, Bioinformatics, Immunology, Computer Science, or a closely related field.
Coursework or research experience in machine learning, statistics, or computational modeling is strongly preferred.
Experience:
Experience in computational biology, bioinformatics, or related research.
Hands-on experience applying machine learning or statistical modeling to biological datasets.
Experience in experimental design, data collection, and analysis in a research setting.
Experience mentoring or guiding students, interns, or junior researchers is a plus.
Participation in collaborative research projects and contribution to publications or presentations.
Working knowledge of machine learning and computational biology, with applications in immunology.
Understanding of experimental design, data structures, and statistical methods relevant to biological research.
Familiarity with research best practices, including reproducibility, metadata standards, and benchmarking frameworks.
Awareness of academic publishing and grant proposal processes.
Experience with data curation, annotation, and analysis, including metadata management and dataset quality control.
Experience managing, annotating, and curating high-quality datasets, including metadata management.
Technical Skills and Knowledge:
Proficiency in programming languages such as Python or R, with experience in scientific computing and data analysis libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow/PyTorch).
Familiarity with version control systems (e.g., Git) and reproducible research practices.
Ability to design, implement, and benchmark computational models, with understanding of appropriate metrics.
Strong analytical and problem-solving skills, with the ability to interpret complex biological data.
Familiarity with scientific communication tools (e.g., LaTeX, PowerPoint) for preparing presentations, manuscripts, and reports.
Preferred Competencies
Ability to design, implement, and benchmark models with rigorous metrics.
Skilled in experimental planning, data interpretation, and literature review.
Capacity to synthesize findings into reports, manuscripts, and presentations.
Strong written and oral communication skills for journal publications, conference presentations, seminars, and grant proposals.
Ability to translate complex technical findings into clear, accessible language for diverse audiences.
Demonstrated ability to mentor students, postdocs, and research staff through technical guidance and code reviews.
Collaborative mindset, with experience working across multidisciplinary teams and with external partners.
Strong organizational skills to establish and enforce lab-wide standards for coding, data management, and documentation.
Ability to scope and launch new research projects while managing multiple priorities.
Commitment to high-quality, reproducible science and ethical research practices.
Willingness to contribute to general lab operations and foster a collaborative, inclusive, and high-performance environment.
Working Conditions
Office Environment.
Application Documents
Resume (required)
Cover Letter (preferred)
When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.
Job Family
Role Impact
Scheduled Weekly Hours
Drug Test Required
Health Screen Required
Motor Vehicle Record Inquiry Required
Pay Rate Type
FLSA Status
Pay Range
The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.
Benefits Eligible
The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.
Posting Statement
The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.
Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.
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