How the Weighting Mechanism for the 10 Dimensions in the A-F Evaluation Scoring System Calculates Job Fit

The A-F evaluation scoring system uses a weighted average of ten IO dimensions—topped by North Star alignment at 25%—to generate a precise 0-5 job-fit score that ranks opportunities against a candidate's profile.

The santifer/career-ops repository implements a sophisticated multi-dimensional scoring algorithm to quantify how well a job posting aligns with a candidate's career objectives. At its core, the weighting mechanism for the 10 dimensions in the A-F evaluation scoring system transforms qualitative assessments from six structured evaluation blocks into a single numeric score. This weighted matrix, defined in modes/ofertas.md, ensures that critical factors like role alignment and CV match exert greater influence than secondary considerations such as interview speed or cultural signals.

The 10 IO Dimensions and Their Percentage Weights

The system evaluates IO dimensions—capturing Inputs from candidate data and Outputs from job expectations—each assigned a specific percentage weight that determines its contribution to the final score. The weights follow a deliberate hierarchy that prioritizes strategic fit over convenience factors, distributed as follows:

• North Star alignment (25%): Measures exact role match, where 5 indicates the target role and 1 indicates unrelated work.
• CV match (15%): Quantifies resume alignment, ranging from 90%+ match (5) to below 40% match (1).
• Level (15%): Assesses seniority fit, from Staff-level (5) to Junior (1).
• Estimated compensation (10%): Evaluates pay band positioning, from top-quartile (5) to below-market (1).
• Growth trajectory (10%): Rates career progression potential, from clear next-level path (5) to dead-end (1).
• Remote quality (5%): Scores flexibility, from full-remote async (5) to on-site only (1).
• Company reputation (5%): Captures employer brand strength, from top employer (5) to red flags (1).
• Tech stack modernity (5%): Measures technology currency, from cutting-edge AI/ML (5) to legacy stack (1).
• Time-to-offer speed (5%): Evaluates hiring process efficiency, from fast process (5) to exceeding six months (1).
• Cultural signals (5%): Assesses workplace environment, from builder culture (5) to bureaucratic (1).

Because the percentages sum to 100%, each weight functions as a decimal multiplier (e.g., 25% = 0.25) in the final calculation.

How the Weighted Score Is Calculated

The total weighted score derives from multiplying each dimension's 1-5 rating by its corresponding weight, then summing the products. Since the weights sum to 1.0, the result requires no additional normalization and directly represents the weighted average.

The calculation follows this formula:

total = Σ (rating_i × weight_i)
finalScore = total / Σ weight_i  // Where Σ weight_i = 1.0

In modes/ofertas.md, this translates to a straightforward weighted average where the denominator equals 1, producing a final score between 0 and 5.

Mapping A-F Evaluation Blocks to Dimensions

The A-F evaluation blocks described in modes/oferta.md serve as data feeders for the weighting matrix, translating unstructured job details into structured ratings:

• Block A (Role Summary) informs the North Star alignment and Level ratings.
• Block B (Match with CV) directly provides the CV match rating.
• Block C (Level & Strategy) refines the Level rating and influences Growth trajectory.
• Block D (Comp & Demand) supplies data for Estimated compensation and Time-to-offer speed.
• Block E (Customization Plan) and Block F (Interview Plan) contribute qualitative context that shapes Company reputation, Tech stack modernity, Remote quality, and Cultural signals.

Implementing the Score Calculation

Here is a practical JavaScript implementation that mirrors the logic in modes/ofertas.md:

// Weight definitions from modes/ofertas.md
const weights = {
  northStar: 0.25,
  cvMatch: 0.15,
  level: 0.15,
  comp: 0.10,
  growth: 0.10,
  remote: 0.05,
  reputation: 0.05,
  techStack: 0.05,
  speed: 0.05,
  culture: 0.05,
};

// Ratings (1-5) derived from A-F evaluation blocks in modes/oferta.md
const ratings = {
  northStar: 5,
  cvMatch: 4,
  level: 3,
  comp: 2,
  growth: 4,
  remote: 5,
  reputation: 4,
  techStack: 5,
  speed: 3,
  culture: 4,
};

// Calculate weighted job-fit score (0-5)
const weightedScore = Object.entries(ratings).reduce(
  (sum, [dimension, rating]) => sum + rating * weights[dimension],
  0
);

console.log(`Job-fit score: ${weightedScore.toFixed(2)}`); // Output: 3.85

This implementation demonstrates how the weighting mechanism prioritizes high-impact dimensions: a perfect 5 in North Star alignment contributes 1.25 points to the final score (5 × 0.25), while a perfect Cultural signals rating contributes only 0.25 points (5 × 0.05).

Summary

• The weighting mechanism uses a 10-dimension matrix defined in modes/ofertas.md to calculate a 0-5 job-fit score.
• North Star alignment (25%), CV match (15%), and Level (15%) carry the highest weights, accounting for 55% of the total score.
• A-F evaluation blocks in modes/oferta.md populate the dimensional ratings that feed into the weighted calculation.
• The final score is a weighted average where each rating is multiplied by its percentage weight and summed.
• Lower-weighted dimensions (5% each) capture secondary factors without diluting the impact of strategic alignment.

Frequently Asked Questions

What file defines the 10-dimension weighting matrix?

The weighting matrix is explicitly defined in [modes/ofertas.md](https://github.com/santifer/career-ops/blob/main/modes/ofertas.md), which serves as the "Multi-Job Comparison" mode configuration. This file establishes the percentage allocations for each IO dimension and the mathematical formula for aggregating ratings into a final score.

How does the A-F evaluation feed into the final score?

The A-F evaluation blocks described in [modes/oferta.md](https://github.com/santifer/career-ops/blob/main/modes/oferta.md) generate the raw 1-5 ratings for each dimension. Block A influences North Star and Level; Block B provides CV match; Block C affects Level and Growth; Block D determines Compensation and Speed; while Blocks E and F supply qualitative inputs for Culture, Tech Stack, Reputation, and Remote quality.

Why is North Star alignment weighted at 25%?

North Star alignment receives the highest weight (25%) because it measures the fundamental strategic fit between the role and the candidate's long-term career objectives. According to the source code analysis, this dimension determines whether the position represents the target role (5) or unrelated work (1), making it the primary driver of job satisfaction and retention.

Can the weights be customized per candidate?

While the base weights are hardcoded in modes/ofertas.md as a standardized framework, the architecture allows for candidate-specific overrides. Since the calculation uses a simple weighted average formula, implementing custom weight objects enables personalized scoring that might, for example, prioritize Remote quality (currently 5%) over Growth trajectory (currently 10%) for candidates with specific constraints.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}