Temporary Research Intelligence Data Science Specialist - Univeristy Of Colorado Boulder

Requisition Number:

74033

Location:

Boulder, Colorado

City

Boulder

State

Colorado

Employment Type:

Temporary Staff

Schedule:

Part-Time

Posting Close Date:

11-Aug-2026

Date Posted:

06-Aug-2026

Job Summary

Industry Research Partnerships is seeking a temporary Research Intelligence Data Science Specialist to develop and document an internal technical prototype of the IRP Research Intelligence MVP. The prototype will help IRP staff search information about university researchers and prospective industry partners, identify potential researcher-company alignments, and understand the evidence supporting recommended matches.

The position will work with approved university and external data to develop the prototype’s data architecture, data-integration processes, researcher and company records, semantic search, bidirectional matching methods, evaluation framework, and technical documentation. The position will collaborate with research intelligence, industry partnership, data, and technology partners and will conclude with a documented demonstration and handoff.

CU is an Equal Opportunity Employer and complies with all applicable federal, state, and local laws governing nondiscrimination in employment. We are committed to creating a workplace where all individuals are treated with respect and dignity, and we encourage individuals from all backgrounds to apply, including protected veterans and individuals with disabilities.

What Your Key Responsibilities Will Be

• Translate defined IRP business questions and workflows into a practical technical plan, prototype architecture, and four-week development backlog.
• Inventory and prepare approved data sources and create a pilot data model covering researchers, companies, opportunities, supporting evidence, provenance, and access restrictions.
• Develop or refine data-ingestion and transformation processes for structured and unstructured information.
• Create searchable researcher and company records using filters, semantic retrieval, or natural-language search methods.
• Prototype faculty-to-company and company-to-CU matching functions that connect recommendations to supporting evidence and appropriate confidence or evidence-quality indicators.
• Test the prototype using representative use cases; identify false positives, omissions, data-quality problems, and known failure modes; and make prioritized refinements.
• Maintain project code, data definitions, assumptions, and documentation in approved shared university locations.
• Prepare a README, data dictionary, known-limitations record, maintenance considerations, prioritized next-phase backlog, and recommendations for possible future scaling.