Data Scientist

The Data Science Institute (DSI) is a campus-wide research institute that is central to the university's strategic priority to grow its research enterprise and expand its global impact. Our team of data scientists, software engineers, and AI engineers works shoulder-to-shoulder with faculty, students, and industry partners on problems across nearly every domain on campus.

A typical week at DSI might include scoping a new collaboration with a principal investigator, pair-programming on a deep learning pipeline with another member of DSI's technical staff, and reviewing a statistical analysis for a health sciences team. The same week could bring teaching a workshop and mentoring a graduate student. The work is varied and the collaborators change, but the throughline remains constant: we bring rigorous, reproducible data science to researchers who need it and help the campus build lasting capacity along the way.

DSI is also a core partner in the Wisconsin Research, Innovation and Scholarly Excellence (RISE) initiative, which is bringing over 150 new faculty to campus over three years and more than doubling investment in AI, sustainability, and health. As RISE expands, so does the demand for data scientists who can meet those faculty where they are and help turn ambitious research questions into impactful results.

Learn more about DSI at https://dsi.wisc.edu/ and RISE at https://rise.wisc.edu/.

Who we're looking for:

We are hiring two to three Data Scientists to join the team, based on-site in Madison. We are looking for candidates who will thrive in a research environment: people who are technically strong, intellectually curious, and energized by the chance to apply their skills across many different domains.

The strongest candidates will bring deep expertise in at least one area of data science. Areas where we are especially looking to add depth include:

• Machine learning and deep learning, including computer vision, large language model (LLM) applications, retrieval-augmented generation (RAG), and agentic workflows, along with model training and production deployment.

• Statistical modeling and uncertainty quantification, including causal inference, Bayesian methods, meta-analysis, and study design.

• Data engineering for research-scale data: ingestion pipelines, distributed storage, and end-to-end handling of large or complex datasets that downstream modeling work depends on.

This list is illustrative rather than exhaustive. Adjacent expertise you think we should know about is welcome in your application. Equally important is enough breadth and curiosity to contribute outside your core area when a project calls for it, and the judgment to know when to do so.

Beyond technical depth, a few things matter to us. A collaborative instinct: our work is almost always in partnership with researchers, and the ability to listen, scope, and explain matters as much as the ability to model. A commitment to reproducible, well-engineered work: we want code that others can run, results others can trust, and methods others can learn from. A sense of stewardship for the data, the collaborators, and the junior colleagues and students who will use what we build. An openness to feedback: the ability to hear criticism as information about the work rather than the person, and to listen past what a collaborator literally asks for to the intent underneath. The strongest data scientists build things people actually use because they keep refining toward what the work actually needs.

• Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators

• Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources

• Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows

• Independently identifies and implements appropriate data science techniques to find data patterns and answer research questions chosen by the lead researcher including data visualization, statistical analysis, machine learning, and data mining

• Organizes and automates project steps for data preparation and analysis

Engages in project intake, scopes new collaborations with researchers, and drafts statements of work for incoming engagements.

Contributes to DSI's programmatic activities, including workshops, training, graduate student mentorship, and the institute's internal life.

Data Science Institute (DSI)

Compensation:

Salary Range: Applicants for this position will be considered for the titles listed in this posting. Title and compensation are determined by the experience and qualifications of the finalist. Early-career finalists can expect an annual salary of $70,000-$85,000; mid-career, $80,000-$110,000; and advanced, $100,000 or more.

Employees in this position can expect generous vacation, holidays, and paid time off, competitive insurance and savings accounts, and retirement benefits through the Wisconsin Retirement System (WRS). DSI also supports professional development funding and flexible work arrangements, where consistent with role responsibilities.

Additional Information:

This Data Scientist position is full or part time (75% - 100%) and posted at levels II and III. The key job responsibilities for the Data Scientist II position are reflected above. The key job responsibilities for the Data Scientist III position can be found here with two additional responsibilities:

- Provides technical leadership on grant proposals for research partners, including scoping, methods, and review. May also lead proposals for candidate-driven research aligned with DSI's mission

- Contributes to DSI's strategic and programmatic activities, including mentorship of junior staff, external representation, and the institute's internal life.

While applications for this position will be submitted at the Data Scientist II level, DSI will have the discretion to hire applicants at the Data Scientist III level. The level will be determined based on the successful applicants' experience and qualifications. An applicant who is transitioning into a data scientist role for the first time, and whose experience has mainly been in the context of graduate or post-graduate research, will likely be considered early career. An applicant with prior professional experience as a data scientist or who has taken on significant technical projects may be considered mid-career. An applicant with significant expertise and several years in a similar role, particularly one involving supervising others, will be considered advanced.

• Demonstrated experience with data science and computational workflows, including data wrangling, cleaning, modeling, visualization, and analysis.

• Demonstrated programming experience in at least one language commonly used for data science (Python, R, Julia, or comparable).

• Demonstrated ability to take a vague research question, sharpen it into something answerable, and deliver results that move the work forward.

• Strong written and oral communication, including the ability to explain technical work to non-technical collaborators and to build consensus across disciplinary boundaries.

• Ability to work independently and as part of a team, set priorities, exercise good judgment about where to invest effort, and adapt as projects evolve.

Technical depth (any subset is welcome - depth in one is more valuable than shallow coverage of all):

• Experience with AI/ML frameworks such as PyTorch, JAX, TensorFlow, or Keras.

• Experience with MLOps tools such as Weights & Biases, MLflow, or Neptune.

• Experience with distributed computing systems: cloud (AWS, Azure, GCP), high-performance computing (HPC), or the UW-Madison Center for High Throughput Computing (CHTC).

• Experience with containerization (Docker), package and release workflows, and continuous integration / continuous deployment (CI/CD) using tools such as GitHub Actions or GitLab CI.

• Experience building stakeholder-facing tools such as dashboards or web portals.

Working practices and collaboration:

• Collaborative Git practice: branches, pull requests, and code review.

• Writing and maintaining automated tests for the code you ship (unit-level at minimum).

• Managing reproducible computing environments using tools such as venv, conda, uv, or pixi.

• Experience working in a research environment and engaging with highly technical researchers across multiple methodological fields, research domains, and computational platforms.

• Experience in a consulting or services role: making first contact, working with a researcher to understand the underlying need, breaking a research problem into pieces with clear deliverables, scoping a statement of work, and managing the project through delivery.

• Experience contributing to grant proposals or other sponsored-research activities.

• Teaching, mentoring, or training experience.

• A track record of positive impact on your local working environment.

The required degree depends on the level at which the candidate is hired. Acceptable disciplines include statistics, computer science, data science, physics, engineering, econometrics, epidemiology, or a research field that leverages data science techniques.

Required for Data Scientist II (RE021): A master's degree in one of the disciplines above.

Required for Data Scientist III (RE061): A PhD in one of the disciplines above.

For the best experience completing your application, we recommend using Chrome or Firefox as your web browser.

To apply for this position, select either "I am a current employee" or "I am not a current employee" under Apply Now. You will then be prompted to upload your application materials.

Important: The application has only one attachment field. Upload the following documents in that field, either as a single combined file or as multiple files in the same upload area.

To be considered, please submit:

• Cover letter (up to two pages)

• Resume or curriculum vitae

Cover letters should address:

• Where your deepest technical expertise lies, with a concrete example of work you are proud of and what made it hard.

• What draws you to DSI specifically: the kind of work, the collaborators, the mission, or something else.

The cover letter is the primary way we will get to know you. We would rather read a thoughtful two pages than a generic letter.

Optional: links to a portfolio, GitHub profile, published work, or deployed tools.

References will be requested only of finalists.

University sponsorship is not available for this position, including transfers of sponsorship and TN visas. The selected applicant will be responsible for ensuring their continuous eligibility to work in the United States (i.e. a citizen or national of the United States, a lawful permanent resident, a foreign national authorized to work in the United States without the need of an employer sponsorship) on or before the effective date of appointment. This position is an ongoing position that will require continuous work eligibility. If you are selected for this position you must provide proof of work authorization and eligibility to work.

The department will not be able to support a request for a J-1 waiver. If you choose to pursue a waiver and apply for our position, neither the UW nor UWMF will reimburse you for your legal or waiver fees.

Ben Ball, research@datascience.wisc.edu

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