Data Scientist I - Computational Research, Statistics & Machine Learning

NORC at the University of Chicago•Chicago, IL

6d•Hybrid

About The Position

NORC at the University of Chicago is seeking a qualified Data Scientist to join the Statistics and Data Science department and support a diverse range of research projects. The Data Scientist I works collaboratively with statisticians, researchers, methodologists, software developers, and subject matter experts to develop statistical, data science, and computational solutions that improve research, operational efficiency, and decision-making. This role combines strong foundations in data management, statistical modeling, machine learning, and computational social science. The position also provides opportunities to apply emerging technologies, including generative AI and large language models (LLMs), to research problems. The Data Scientist I will contribute to projects involving structured and unstructured data, survey data, administrative records, commercial data, text data, and other novel data sources. Responsibilities may include developing analytical workflows, building statistical and machine learning models, creating data products and applications, and evaluating AI approaches for research and operational use. The ideal candidate possesses strong Python programming skills, experience building reproducible analytical workflows, and an interest in applying advanced computational, statistical, and AI methods to solve complex research and business problems.

Requirements

Bachelor's degree in computational social science, data science, statistics, computer science or a related quantitative field.
4 years of relevant experience including graduate research, internships, and applied professional experience.
Demonstrated proficiency developing production-quality code in Python.
Strong experience with SQL and relational databases.
Experience developing software, data pipelines, or analytical applications.
Experience conducting statistical analysis and machine learning using real-world datasets.
Knowledge of supervised and unsupervised machine learning methods.
Experience working with Git and collaborative development workflows.
Strong problem-solving and analytical skills.
Excellent communication and technical writing skills.
Ability to explain technical concepts to diverse audiences.
Qualified applicants must be eligible to work in the U.S.
Please include a Resume and Cover Letter when applying.
Candidates should be prepared to provide professional references upon request.

Nice To Haves

Experience with large-scale data platforms such as Databricks, Spark, Hadoop, or Hive.
Experience developing, evaluating, or deploying machine learning and AI solutions, including work with large language models, and cloud environments (e.g., AWS, Azure, GCP).
Experience applying data disclosure limitation (SDL) and data privacy methods, including risk assessment and mitigation for public data releases.
Experience with R or SAS
Experience analyzing healthcare, survey, administrative, or other large-scale observational datasets, especially healthcare claims data.
Experience with record linkage.

Responsibilities

Apply statistical, computational, and machine learning methods to support survey research, evaluation studies, and other social science research initiatives
Establish reproducible, quality‑assured analytic workflows; implement version control (Git), environment management, peer code review, automated testing, and validation checks.
Develop robust data pipelines that integrate, transform, and analyze structured and unstructured data from multiple sources to support research and operational objectives.
Design and implement data products, dashboards, applications, and automated tools that enable data-driven decision making.
Apply machine learning, AI, and natural language processing methods and collaborate with multidisciplinary teams to design, evaluate, and deploy AI-enabled analytic applications.
Write and implement R, and Python programs to extract/manipulate data, link complex datasets, and execute statistical and machine learning analyses.
Work with relational databases, cloud platforms, and large-scale computing environments to manage and analyze complex datasets efficiently.
Uphold data disclosure limitation and data privacy best practices; apply statistical disclosure limitation for public releases and restricted-use data.
Perform other duties as assigned.

Benefits

Generously subsidized health insurance, effective on the first day of employment
Dental and vision insurance
A defined contribution retirement program, along with a separate voluntary 403(b) retirement program
Group life insurance, long-term and short-term disability insurance
Generous paid time off
Holidays
Paid parental leave
Bereavement leave
Tuition assistance
An Employee Assistance Program (EAP)