at University of Chicago in Chicago, Illinois, United States
Job Description
DepartmentBSD DFI - Translational Informatics
About the Department
The Duchossois Family Institute (DFI) at the University of Chicago is using cutting-edge technologies and developing facilities to accelerate research and introduce novel interventions that optimize microbiome-mediated host defenses, with the goal of enhancing health. By developing new knowledge about the interactions between the human body and the microbiome, the DFI is breaking new ground that will lead to therapies that increase resistance to a wide range of diseases.
Job Summary
The Duchossois Family Institute focuses on the role of commensal bacteria in enhancing disease resistance and is using genomic and metabolomic platforms to characterize the microbiome and its metabolites. At DFI, we rely on a dynamic team of programmers, data scientists and engineers to handle our rapidly growing biomedical data. We are seeing a full-stack data engineer who will work with new technologies and architectures to develop scalable analytical applications that will help in organizing, managing, and extracting meaningful inferences for a better understanding of the data. Experience with biomedical data and computational expertise is required to build custom inward and outward facing applications with advanced AI/ML approaches and robust visualization. The ideal candidate has experience building products across the stack and a firm understanding of web frameworks, APIs, databases, visualization frameworks, and multiple back-end languages. The Data Science, Sr. Analyst. will work as part of a translational informatics team.
Responsibilities
- Participate in agile software development, including design, implementation, and deployment.
- Design and develop robust and reusable visualization components using advanced Web technologies and frameworks.
- Designing and implementing scalable web services, applications, and APIs to facilitate better understanding of all the biomedical data generated at the DFI.
- Contribute to the development of an integrated knowledgebase (Relational Database) integrating different omics data types and clinical data.
- Transforming data into new formats to make it more appropriate for analysis.
- Has working knowledge of multiple programming languages and statistical packages, as well as knowledge of dataset tools and Artificial Intelligence/Machine Learning (AI/ML) tools.
- Develop Python and JavaScript libraries for the visualization of microbiome, metagenome, metabolome and other biomedical data types generated at the DFI.
- Work in collaboration with others to complete projects in a timely and efficient manner.
- Support scalable bioinformatics workflows for the end-to-end data analysis of high throughput microbiome, metabolome, and clinical data.
- Customize existing applications to meet specific scientific project needs.
- Assist in creating data algorithms and specialized computer software to identify and classify components of a biological system (e.g. DNA and protein sequences).
- Support the development of data models, databases, and software applications.
- Produce, present, and discuss high quality data analysis reports.
- Participate in the communication of results through scientific publications.
- Develop domain-specific languages to describe bioinformatics workflows.
- Run parallelizable big data workflows on cloud platforms such as AWS, Azure, Google.
- Automate the deployment of DFI APIs and web applications using Docker and other CI/CD technologies.
- Participates in creating data algorithms and specialized computer software to identify and classify components of a biological system (i.e. DNA and protein sequences).
- Applies basic application of computational tools and information technology to gather, analyze and visualize data in biology and biomedical research.
- Interprets data analysis of high throughput microbiome, metabolome, genetic and other biomedical data.
- Plans own resources to implement or modify existing web-based applications.
- 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.
- Builds and analyzes statistical models and reproducible data processing pipelines using knowledge of best practices in machine learning and statistical inference. Serves as a single point of contact for all requests and engages other IT resources to assist as needed. May partner with other campus teams to assist faculty with data science related needs.
- Performs other related work as needed.
Minimum Qualifications
Education:
Minimum requirements include a college or university degree in related field.
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Work Experience:
Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.
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Certifications:
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Preferred Qualifications
Education:
- Masters degree in computer science or related field.
Preferred Competencies
- At least two years of experience in building large-scale web applications.
- Experience in developing analytical research-based platforms.
- Experience with Nextflow to create highly customizable workflows, especially within Biomedical informatics.
- High proficiency in building complex web applications with React and Typescript (knowledge of other front-end technologies such as Angular, Flask, etc. can help).
- Excellent knowledge of containerizing and deploying applications using Docker/Docker Compose.
- Knowledge of multiple back-end languages (e.g. Python, NodeJS).
- Excellent knowledge of relational databases (e.g. MySQL, Postgres), web servers (e.g. Nginx) and UI/UX design.
- Experience in implementing, designing, and integrating RESTful APIs.
- Familiarity with XML, JSON, PowerShell, batch scripting proficiency with Git.
- HPC/Cloud computing and distributed architectures.
- Excellent debugging and optimization skills.
- Experience in unit/integration testing.
- Experience in developing high-level domain-specific languages.
- Knowledge of running and parallelizing big data workflows on cloud platforms such as AWS, Azure, Google.
- Must work closely with students, postdoctoral fellows and faculty members.
- Must pay great attention to detail and help investigators design studies that will provide useful and ultimately publishable data.
- Must have flexibility in order to provide analyses that, in some circumstances, are required for grant or presentation deadlines.
Application Documents
- Resume (required)
- Cover Letter (preferred)
When applying, the document(s) MUSTbe uploaded via the My Experience page, in the section titled Application Documents of the application.
Job Family
Research
Role Impact
Individual Contributor
FLSA Status
Exempt
Pay Frequency
Monthly
Scheduled Weekly Hours
40
Benefits Eligible
Yes
Drug Test Required
No
Health Screen Required
No
Motor Vehicle Record Inquiry Required
No
Posting Statement
The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, national or ethnic origin, 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.
Staff 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.
We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages a diversity of perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange.
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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