Data Scientist

uchicago; Chicago (IL); On-site; USD 70,000 - 100,000; Full time

Job summary

The University of Chicago is seeking a Data Scientist to lead data science research within the breast cancer program of the Hematology/Oncology Section. You will build multi-modal data frameworks and ML pipelines, working with collaborators to apply cutting-edge computational tools.

Ideal candidates have 2–5 years of relevant experience, strong Python skills (Torch, scikit-learn), and familiarity with imaging or unstructured text data.

Qualifications

• Advanced degree in computer science or related field.
• Experience with Python, Torch and scikit-learn libraries.
• Experience with image data or unstructured text data with large language models.

Responsibilities

• Lead data science research efforts within the breast cancer program.
• Develop multi-modal data frameworks (genomic, pathology, radiographic).
• Create data dictionaries and pipelines for data processing and analysis (ML).
• Collaborate to implement external computational tools for program data.
• Draft grant proposals and develop training materials.
• Supervise trainees and ensure reproducible analyses.

Skills

Linux/Ubuntu

tmux

SLURM

Analytical skills

Problem-solving

Organizational skills

Communication skills

Teamwork

Education

Advanced degree in computer science

Bachelor's degree in related field

Tools

Python

Torch

scikit-learn

Image data

Job description

Department

BSD MED - Hematology and Oncology - Howard Research Staff

About the Department

The Section of Hematology/Oncology has a proud and long tradition of excellence in research-based patient care and clinical discovery. Ranked among the finest cancer programs in the country, the Section is comprised of nationally and internationally known faculty with expertise in all major types of malignancies, blood disorders, and experimental therapies.

Job Summary

The Data Scientist will lead data science research efforts within breast cancer program in the Section of Hematology/Oncology in the Department of Medicine.

Responsibilities

• Manage the program's projects.
• Develop frameworks for housing multi-modal clinical data - including genomic, pathology, and radiographic imaging data.
• Develop and code data dictionaries for discrete data.
• Write, Implement, and develop efficient code and machine learning tools and pipelines for data processing and analysis.
• Work with collaborators to implement emerging external computational tools for program data.
• Draft grant proposals.
• Develop training material.
• Keep lab's knowledge-based trainings up to date.
• Supervise trainees.
• Perform reproducible genomic data analysis.
• Calibrates data between large and complex research and administrative datasets. Guides and may set the operational protocols for collecting and analyzing information from the University's various internal data systems as well as from external sources.
• Designs and evaluates statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference.
• Provides expertise for high level or complex data-related requests and engages other IT resources 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: Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.
• Certifications: ---

Preferred Qualifications

• Education: Advanced degree in computer science.
• Experience: Experience in python including torch and scikit-learn libraries strongly preferred.
• Experience: Experience in working with image data or unstructured text data with large language models.

Preferred Competencies

• Proficiency in Linux/Ubuntu OS including basic file and user management, ssh.
• Familiarity with tmux, SLURM.
• Strong Analytical skills.
• Problem-solving skills.
• Organizational skills.
• Verbal and written communication skills.
• Ability to work independently and as part of a team.

The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.

Job Family

Research

Role Impact

Individual Contributor

Scheduled Weekly Hours

40

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

FLSA Status

Exempt

Pay Range

$70,000.00 - $100,000.00 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

Yes 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 t