Key Takeaways

• STARs already hold 17% of data science roles, but face rising degree requirements that limit access.
• Research identified key “origin jobs” with transferable skills and mapped pathways into data roles.
• Employers, training providers, and STARs each use different frameworks—leading to misalignment in hiring and development.
• The project produced four solution concepts, including on-the-job learning and a common career map for data science.
• Tactical interventions were co-designed with stakeholders and are being tested through pilots and prototypes.

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Case Summary

Data science careers offer high wages and strong growth—but remain largely out of reach for STARs (workers Skilled Through Alternative Routes) due to rising degree requirements and unclear pathways. With support from the Patrick J. McGovern Foundation, Opportunity@Work set out to change that, using labor market analytics, stakeholder interviews, and co-design workshops to uncover where the barriers lie—and how to overcome them.

The research revealed that STARs are already succeeding in data science roles like operations research analysts, but often self-select out of the field due to intimidating job titles, lack of access to training, and hiring practices that over-index on degrees. To address these issues, the team developed and tested four solution concepts: a shared data science career map, a national awareness campaign, peer learning networks, and internal upskilling strategies for employers.

Each concept was validated with employers, talent developers, and STARs themselves. Together, they offer practical, scalable approaches to grow a more inclusive data science workforce—and help employers meet their growing demand for skilled, diverse talent.