How is the Job Score determined?

Datapeople’s all-in-one Job Score predicts how a job post is likely to perform with qualified candidates based on its title, content, and language. The score updates dynamically as you write, helping you shape a post that is clear, relevant, and accessible to a broad pool of applicants.

How the Job Score reflects predicted performance

The Datapeople score incorporates signals associated with candidate search intent, title-market alignment, and clarity of expectations: all of which influence how effectively a post performs in today’s labor market. Posts that align more cleanly with how candidates search and assess roles tend to appear in more relevant searches and convert more qualified job seekers earlier in the funnel.

Clear, well-structured job posts also strengthen employer brand by signaling competence and professionalism at the first point of candidate contact. Across our dataset, jobs that score 85+ are consistently associated with stronger qualified and more representative applicant pools.

When we analyze a job, we look at a set of core questions that directly inform its score:

• Will qualified candidates be able to find your job?

  We evaluate the title, location, and seniority to understand how well the job aligns with how candidates search.

• Will qualified candidates understand what the job actually is?

  We check for alignment between the job title and its requirements, and analyze language structure and word choice for clarity.

• Will every qualified candidate feel welcome?

  We assess whether the post includes unnecessary physical requirements, language with unintended bias, or phrasing that may discourage qualified candidates from underrepresented groups.

These questions simplify a much larger set of signals that influence your Job Score.

What informs the score

The scoring framework draws on several categories of signals, including search-pattern data from candidates, linguistic clarity indicators, job-structure cues, labor-market context, and inclusion-related language patterns. These high-level dimensions help the model evaluate how effectively a job post communicates with its intended audience. Other factors, including job length, predicted candidate pool size, and industry patterns, also contribute to ensuring that a score of 85+ reflects a job that is clear, well-structured, and aligned with how candidates search and evaluate roles.

How job content supports employer brand

Candidates often form their first impression of an organization through its job posts. Clarity, structure, and a well-organized description signal professionalism, operational maturity, and respect for the candidate’s time. The Job Score reflects whether the post reinforces the employer’s brand through accessible language, well-framed responsibilities, and a coherent narrative about the role.

How the model evolves

The scoring framework is reviewed and updated regularly to reflect current labor-market conditions and emerging candidate behavior patterns. This ensures that the benchmarks behind the score remain current and grounded in real-world performance.

What the Job Score isn’t

The Job Score doesn’t predict recruiter performance, hiring manager decisions, or final hiring outcomes. It evaluates the clarity, structure, and inclusiveness of the job post itself, not the broader recruiting process. It’s designed to guide better writing and market alignment, not to override professional judgment.

Job Score vs Inclusion Meter

The Job Score reflects the overall expected performance of a job post based on its title, content, language, and the local labor market. It incorporates a wide range of predictors beyond inclusion alone. Jobs that score 85+ tend to attract stronger, more representative applicant pools across our dataset.

The Inclusion Meter focuses specifically on the clarity and inclusiveness of your language. It identifies potential barriers related to gender, race, physical ability, age, nationality, socioeconomic background, and religion. It supports you in writing posts that reduce unintended exclusion.

Ensuring consistency across hiring teams

Beyond individual roles, the Job Score supports broader organizational consistency and operational rigor. Our scoring framework also helps teams maintain consistent quality across hiring managers and departments. By grounding evaluation in shared standards of clarity and structure, the Job Score reduces variability in job-post quality and helps organizations present a unified, credible employer brand at scale.

How We Validate the Job Score

Datapeople’s scoring methodologies are continuously evaluated against real hiring outcomes. Several independent evidence sources support the predictive strength of the model:

• Large-scale market data: Our proprietary job search engine has allowed us to collect and analyze both job and candidate-behavior data for more than five years. To date, we’ve analyzed over 110M job posts from more than 20,000 employers.
• Performance patterns across the market: Across our dataset, higher-scoring jobs are regularly associated with sharper applicant-job fit, earlier funnel conversion, and fewer unqualified applicants. These patterns reflect the relationship between job-post clarity and how candidates filter, evaluate, and commit to roles during the job-search process.
• Candidate behavioral research: We conduct research to measure how candidates from different backgrounds react to job-post language and content. This work helps us understand how clarity and inclusiveness shape applicant behavior. Here’s one study our data and behavioral-science team has conducted.
• Customer results: Customers consistently report that higher-scoring jobs attract stronger and more representative applicant pools.

Where results vary:

Although the model performs consistently across large datasets, lift can vary by job family, geography, seniority, hiring volume, and the level of flexibility in role requirements. Highly specialized or compliance-driven roles may behave differently from high-volume or generalist roles, and customers may see different levels of improvement depending on their baseline processes.

How We Understand Diversity Outcomes

Higher-quality content improves performance across the funnel by helping qualified candidates self-assess more accurately and engage earlier. Datapeople focuses on improving clarity and content quality at the top of the funnel because the makeup of the applicant pool is one of the strongest predictors of downstream representation. Across our dataset, higher-scoring posts are regularly associated with broader, more balanced applicant pools, though outcomes always depend on individual hiring processes and market conditions.

Our approach to responsible AI

The scoring framework undergoes ongoing review for fairness and unintended bias. We evaluate how the model behaves across job types, industries, and identity groups to ensure its guidance supports equitable, non-deterministic decision-making. The score is designed to assist teams in writing clearer, more inclusive job posts, never to guarantee or dictate specific hiring outcomes.