Scoring & Weights

How the Aurora Match Score Works

Aurora’s Match Score quantifies how well a candidate’s cognitive ability and personality profile align with the demands of a specific role.

It is derived from industry-wide benchmarks, built on open-source databases containing over 1 million profiles and more than 1,000 job categories.

These benchmarks represent typical psychological profiles associated with successful performance in different occupations.

When a job category includes fewer than 100 valid profiles, Aurora merges it with closely related categories to ensure robust statistical reliability. This is why, in some cases, benchmarks for similar roles (e.g. marketing coordinator and communication specialist) may appear identical or nearly identical.

Cognitive Ability Weighting

Cognitive ability is always positively correlated with job performance and is the single strongest individual predictor across all categories.

However, its relative importance varies depending on the complexity and cognitive load of the role.

In analytical or problem-solving roles (e.g. software engineering, data science, strategy), high cognitive ability is crucial and heavily weighted.
In routine or highly structured roles (e.g. customer support, operations), it still contributes to performance but plays a smaller relative role compared to behavioral traits.

This adaptive weighting ensures that Aurora evaluates candidates relative to the demands of the position, not against a fixed or universal ideal.

Personality Trait Weighting

Personality traits influence how people perform — how they interact, persist, and adapt to challenges.

Each Big Five trait can have positive or negative correlations with job performance depending on the category.

For example:

A salesperson typically benefits from high Extraversion and Agreeableness, which support energy, communication, and relationship-building.
A software engineer, in contrast, may perform better with lower Extraversion and higher Openness and Conscientiousness, supporting deep focus, problem-solving, and sustained effort.

Some relationships are more consistent across roles:

Conscientiousness almost always correlates positively with performance, reflecting reliability and goal orientation.
Neuroticism almost always correlates negatively, as emotional volatility tends to reduce focus and stress tolerance in most work environments.

Aurora’s algorithm dynamically adjusts these trait weights based on the empirical relationships found in our benchmark database for each job category.

Distribution and Score Interpretation

The Aurora Match Score follows a theoretical normal distribution with:

Mean = 60
Standard Deviation = 15

This scale allows intuitive interpretation and consistent comparison across roles and candidates:

Percentile Range	Approx. Score Range (Aurora scale)	Interpretation
< 1%	< 25	Not a fit
1% – 10%	≈ 25 – 41	Not a fit
10% – 30%	≈ 41 – 52	Weak fit
30% – 70%	≈ 52 – 68	Decent fit
70% – 90%	≈ 68 – 79	Good fit
90% – 97%	≈ 79 – 88	Very good fit
97% – 99%	≈ 88 – 95	Excellent fit
99% – 99.7%	≈ 95 – 101	Incredible fit
≥ 99.7%	≥ 101	Top fit — The best you will find!

A higher score indicates a stronger match between the candidate’s profile and the target benchmark — not necessarily “better overall,” but better suited for that specific role.

Understanding Score Calibration

While Aurora’s match scores follow a normal distribution, our current dataset is not yet fully representative. Most of the people who have taken our tests so far come from technical and analytical roles — such as engineers, technical recruiters, and sales developers.

These groups usually have above-average problem-solving ability, so their real average score would likely be higher than 60 in a general population.

Because of this, our tests currently feel harder than expected, and some scores may look lower in absolute terms than they should. What matters most is not the raw number, but how a candidate compares to others in the same benchmark or job category.

As we collect more data across different industries and roles, we will update and recalibrate our scoring range to make it fully representative.

Role-Specific Match Scores

Each candidate receives different Match Scores across different job categories.

For example, an individual may score:

78 (Top 10%) for Data Analyst, due to high cognitive ability and analytical personality traits,
but 61 (Top 45%) for Sales Executive, where their lower Extraversion reduces alignment.

This reflects one of Aurora’s core principles: no candidate is universally ideal — success depends on context and fit.

Our scoring system highlights where each candidate is most likely to thrive, not just how well they perform on average.

Custom Benchmarks & Internal Talent Calibration

Beyond industry benchmarks, Aurora enables organizations to create custom, data-driven benchmarks based on their own teams.

By assessing current employees, companies can discover:

The average personality and cognitive profile of their workforce
The traits most common among top performers
The psychological diversity within and between teams
Which traits or abilities differentiate success in specific roles

This process allows Aurora to build organization-specific benchmarks that go beyond generic data — identifying what excellence looks like inside your company.

Once these internal benchmarks are established, Aurora can personalize its scoring algorithms to your culture, values, and strategic needs.

For instance:

If your best-performing salespeople show high Extraversion and low Neuroticism, Aurora can adjust future candidate scores accordingly.
If your engineering teams succeed with high Openness and moderate Conscientiousness, the model will prioritize those characteristics in future matches.

From Hiring to Internal Talent Management

Aurora’s upcoming Internal Talent Management module extends these principles beyond recruitment.

By periodically assessing internal employees, companies can:

Map cognitive and personality strengths across teams
Identify potential future leaders or role transitions
Support targeted training, mobility, and succession planning
Align teams based on complementary cognitive and behavioral profiles

This transforms Aurora from a selection platform into a continuous talent intelligence system — enabling organizations to not only hire effectively but also develop and retain high-performing teams.

👉 Learn more about Aurora’s upcoming Talent Management features →

Scoring & Weights

How the Aurora Match Score Works

Aurora’s Match Score quantifies how well a candidate’s cognitive ability and personality profile align with the demands of a specific role.

It is derived from industry-wide benchmarks, built on open-source databases containing over 1 million profiles and more than 1,000 job categories.

These benchmarks represent typical psychological profiles associated with successful performance in different occupations.

Cognitive Ability Weighting

Cognitive ability is always positively correlated with job performance and is the single strongest individual predictor across all categories.

However, its relative importance varies depending on the complexity and cognitive load of the role.

In analytical or problem-solving roles (e.g. software engineering, data science, strategy), high cognitive ability is crucial and heavily weighted.
In routine or highly structured roles (e.g. customer support, operations), it still contributes to performance but plays a smaller relative role compared to behavioral traits.

This adaptive weighting ensures that Aurora evaluates candidates relative to the demands of the position, not against a fixed or universal ideal.

Personality Trait Weighting

Personality traits influence how people perform — how they interact, persist, and adapt to challenges.

Each Big Five trait can have positive or negative correlations with job performance depending on the category.

For example:

A salesperson typically benefits from high Extraversion and Agreeableness, which support energy, communication, and relationship-building.
A software engineer, in contrast, may perform better with lower Extraversion and higher Openness and Conscientiousness, supporting deep focus, problem-solving, and sustained effort.

Some relationships are more consistent across roles:

Conscientiousness almost always correlates positively with performance, reflecting reliability and goal orientation.
Neuroticism almost always correlates negatively, as emotional volatility tends to reduce focus and stress tolerance in most work environments.

Aurora’s algorithm dynamically adjusts these trait weights based on the empirical relationships found in our benchmark database for each job category.

Distribution and Score Interpretation

The Aurora Match Score follows a theoretical normal distribution with:

Mean = 60
Standard Deviation = 15

This scale allows intuitive interpretation and consistent comparison across roles and candidates:

Percentile Range	Approx. Score Range (Aurora scale)	Interpretation
< 1%	< 25	Not a fit
1% – 10%	≈ 25 – 41	Not a fit
10% – 30%	≈ 41 – 52	Weak fit
30% – 70%	≈ 52 – 68	Decent fit
70% – 90%	≈ 68 – 79	Good fit
90% – 97%	≈ 79 – 88	Very good fit
97% – 99%	≈ 88 – 95	Excellent fit
99% – 99.7%	≈ 95 – 101	Incredible fit
≥ 99.7%	≥ 101	Top fit — The best you will find!

A higher score indicates a stronger match between the candidate’s profile and the target benchmark — not necessarily “better overall,” but better suited for that specific role.

Understanding Score Calibration

These groups usually have above-average problem-solving ability, so their real average score would likely be higher than 60 in a general population.

As we collect more data across different industries and roles, we will update and recalibrate our scoring range to make it fully representative.

Role-Specific Match Scores

Each candidate receives different Match Scores across different job categories.

For example, an individual may score:

78 (Top 10%) for Data Analyst, due to high cognitive ability and analytical personality traits,
but 61 (Top 45%) for Sales Executive, where their lower Extraversion reduces alignment.

This reflects one of Aurora’s core principles: no candidate is universally ideal — success depends on context and fit.

Our scoring system highlights where each candidate is most likely to thrive, not just how well they perform on average.

Custom Benchmarks & Internal Talent Calibration

Beyond industry benchmarks, Aurora enables organizations to create custom, data-driven benchmarks based on their own teams.

By assessing current employees, companies can discover:

The average personality and cognitive profile of their workforce
The traits most common among top performers
The psychological diversity within and between teams
Which traits or abilities differentiate success in specific roles

This process allows Aurora to build organization-specific benchmarks that go beyond generic data — identifying what excellence looks like inside your company.

Once these internal benchmarks are established, Aurora can personalize its scoring algorithms to your culture, values, and strategic needs.

For instance:

If your best-performing salespeople show high Extraversion and low Neuroticism, Aurora can adjust future candidate scores accordingly.
If your engineering teams succeed with high Openness and moderate Conscientiousness, the model will prioritize those characteristics in future matches.

From Hiring to Internal Talent Management

Aurora’s upcoming Internal Talent Management module extends these principles beyond recruitment.

By periodically assessing internal employees, companies can:

Map cognitive and personality strengths across teams
Identify potential future leaders or role transitions
Support targeted training, mobility, and succession planning
Align teams based on complementary cognitive and behavioral profiles

👉 Learn more about Aurora’s upcoming Talent Management features →