Introduction to Aurora
Why Aurora Exists
The traditional hiring process often relies on résumé heuristics that correlate poorly with actual job performance. Recruiters frequently evaluate applicants based on institution names, job titles, or years of experience — signals that are at best weak predictors of future success and can systematically disadvantage applicants from non-traditional backgrounds.
Aurora was developed to provide a fairer, more predictive approach to hiring by measuring the psychological traits that research shows are strong predictors of workplace performance. The platform focuses on measurable indicators of potential (for example, cognitive ability and personality) rather than pedigree.
Aurora's approach predicts up to 55% of variance in job performance, compared to traditional CV screening (approximately 3%) and structured interviews (around 34% - though this heavily depends on interviewer ability and is prone to biases and reinforcement of local patterns).
What Aurora Does
Aurora evaluates candidates through rigorously designed assessments and combines the resulting measures into a single, interpretable Fit Score for each role. The product workflow is:
- Applicants complete a set of assessments (cognitive tasks, validated personality inventories).
- Scores are scaled and combined according to role-specific weights and benchmarks.
- Hiring teams receive a ranked and explainable shortlist with guidance for next steps.
The Science Behind Aurora
Aurora’s models are grounded in the behavioral-science and psychometric literature. Two bodies of evidence are most important for our approach:
-
Cognitive ability (general mental ability) — Cognitive ability is one of the strongest single predictors of job performance across a wide range of roles. For a comprehensive review, see [Schmidt & Hunter, 1998], "The validity and utility of selection methods in personnel psychology" (Psychological Bulletin).
-
Personality traits (particularly the Big Five) — Personality dimensions influence how people apply their cognitive abilities in workplace settings (for example, conscientiousness correlates with persistence and reliability). Combining cognitive and personality measures increases overall predictive validity compared with either source alone [Salgado et al., 2023].
When combined appropriately, cognitive and personality measures provide substantially better prediction of job performance than résumé-based heuristics or unstructured interviews.
Visit the science pageWhy We Don’t Rely on Résumé Heuristics
Common résumé signals such as university attended, prior job titles, or years of experience tend to explain only a small fraction of variance in job performance (typical correlation around r ≈ 0.10). These signals also amplify socio-economic and educational inequalities.
Aurora reduces dependence on those heuristics by prioritizing validated, role-relevant measures of potential that are less correlated with background variables and more directly related to observable workplace behaviors.
Mission
Aurora’s mission is to help organizations build high-performing, inclusive teams through measurement and transparency. We aim to:
- Surface candidates with the strongest potential for a role, regardless of background.
- Provide interpretable scores and actionable insights for hiring teams.
- Continuously validate and refine assessments with outcome data and peer-reviewed research.
What the Aurora Score Represents
The Aurora Score is an aggregated, role-specific index that summarizes a candidate’s alignment with the competencies and behaviors associated with success in a given role. Key properties:
- Normalization: the score distribution is scaled for interpretability (example: mean = 60, SD = 15).
- Higher scores indicate greater predicted fit and potential for success in the target role.
- Integrates cognitive measures, personality profiles, and role-specific benchmarks, and is accompanied by transparency notes that explain which components contributed most to the score.