Staff Data Scientist & AI Researcher

University of Chicago•Chicago, IL

4d•Hybrid

About The Position

The Center for Translational Data Science (CTDS) at the University of Chicago is seeking a Staff Data Scientist to support a diverse range of research projects. Data Scientists work in a collaborative interdisciplinary team and play a critical role in AI/ML tooling, features, and improvements for our open-source software systems and applications, analyzing data, and in understanding and representing user requirements to internal and external stakeholders in our translational data science projects and products. Under the leadership of team or project leads, a person in this position will be a key contributor to the design and implementation of algorithms, AI/ML models, and workflows to enable the discovery of valuable information in large volumes of data from various sources; organizes, harmonizes, and analyzes data sets and develops tools to assist such processes; uses various technologies to visualize data or enable data visualization; and creates applications of general value to the project and product owners. The job uses best practices and advanced knowledge of data manipulation, statistical applications, programming, analysis and modeling in order to implement projects related to the University's various internal data systems as well as from external sources. This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Requirements

Minimum requirements include a college or university degree in related field.
Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Nice To Haves

Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Bioinformatics, or a relevant quantitative field.
Experience working in data science roles, preferably in an academic, research, or health/science environment.
Experience with collaborative open-source projects and software engineering best practices.
Experience working in multi-disciplinary academic teams.
Knowledge of biomedical and translational research data sources is a significant advantage.
Knowledge of hardware specifications required for AI/ML research and projecting resource needs and use in public clouds environments like AWS and GCP.
Knowledge of and experience with data and applications of interest to CTDS, including, but not necessarily limited to, AI data commons, AI data meshes, Gen3, NCI Genomic Data Commons, cancer genomics, biomedical imaging data, human clinical data.
Expertise in designing and implementing machine learning, deep learning, and statistical models for research and production.
Experience with biomedical data.
Experience collaborating on manuscripts and submitting papers for peer-reviewed, scientific publications.
Advanced skills in problem solving and quantitative/qualitative analysis.
Able to organize and prioritize work assignments to meet project needs and work independently to identify and address needs beyond assigned tasks.
Strong communication skills to effectively convey complex findings while serving as a reference and subject matter expert for staff, faculty members, and Gen3 users.
Ability to quickly comprehend requirements and assignments and then explain his/her solutions in both writing and speech.
Ability to learn new skills quickly and manage complex projects.
Proficiency in Python, R, and other relevant programming languages; experience with open-source data science platforms, technologies, and cloud environments.
Outstanding analytical, problem-solving, and project management abilities.
Proven track record mentoring and leading project teams and communicating technical issues to diverse audiences.
Excellent written and verbal communication skills.

Responsibilities

Leading the Interpretation of data from multiple sources.
Developing and implementing software programs and services, software notebooks and software scripts for data transformation, data integration, data analysis and data visualization.
Building, validating and evaluating AI/ML models.
Contributing to and taking a leadership role in the enhancement and maintenance of previously developed in-house open-source data platforms, systems, applications and notebooks.
Performing various types of analysis involving multiple data sets.
Leading data science projects and initiatives within purview, by relaying data analysis and model deployment best practices, enhancing the technical knowledge of peers, and helping develop data science skills in junior employees and interns.
Leading the conceptualization, design, and execution of sophisticated data science projects and AI/ML solutions for research and production environments.
Establish and enforce data governance, quality assurance processes, and operational protocols for large, complex data sets from internal and external sources.
Assisting in providing leadership for design of user-facing computational resources.
Serving as a reference for staff, faculty members, and Gen3 users as a technical subject matter expert by applying principles of data science to define and scope data science projects that involve developing computational tools and services for data engineering, data manipulation, statistical analysis, and modeling.
Collaborating closely with faculty, researchers, and stakeholders to translate scientific and user requirements into actionable data science strategies and solutions.
Stay current with developments in data science, machine learning, artificial intelligence, and related fields, and adopt new methods and technologies to advance research goals.
Communicate complex technical concepts and project results clearly to technical and non-technical audiences, and present findings at internal and external forums.
Has a deep understanding of methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information.
May develop and maintain infrastructure that connects data sets.
Guides staff or faculty members in defining the project and applies principals of data science in manipulation, statistical applications, programming, analysis and modeling.
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.
Performs other related work as needed.