Algorithm Architecture

    How PathScore Finds
    Your Hidden Careers

    Three scoring layers multiply together. Each layer answers a different question. The result: career matches ranked by fit, desire, and discovery.

    Base Match
    ×
    Preference Boost
    ×
    Diversity Bonus
    =
    PathScore

    What We Collect From You

    Everything the algorithm needs to score careers

    Your Occupation

    Matched to O*NET title

    "I work at Amazon warehouse" → Stockers and Order Fillers

    Your Skill Profile

    Skill and expertise dimensions

    Coordination 2.5, Active Listening 2.8, Monitoring 2.5 …

    Hidden Skills

    Side projects, hobbies, languages

    "I sell electronics on eBay" → Sales, Negotiation, E-commerce

    AI Enrichment

    Context extracted from your words

    "lead picker at Amazon" → Team leadership, Performance metrics

    Your Priorities

    What matters most to you

    Higher salary, escape physical labor, work-life balance

    Risk Tolerance

    How much change you can handle

    Play it safe → Moderate → Swing for the fences

    Fed into 3 scoring layers

    1

    Base Match Score

    "Could this person actually do this job?"

    Skill Similarity

    Compares your skill scores to what the job requires. Like measuring how much two puzzle pieces overlap.

    35%

    Knowledge Similarity

    Do you already know the subject areas this job needs? Transportation, math, customer service, etc.

    25%

    Ability Match

    Physical and cognitive abilities — strength, spatial reasoning, attention to detail.

    15%

    Gap Size

    How much training would you need? Smaller gaps = higher score. A tiny stretch beats a huge leap.

    15%

    Hidden Skills Bonus

    This is where the magic happens. Your eBay business maps to "Sales & Marketing" and "Negotiation" — which lights up careers you'd never have found otherwise.

    10%

    Example: Warehouse Worker → Logistics Coordinator

    Skill similarity
    72%
    Knowledge match
    65%
    Gap size (small = good)
    58%
    Hidden skills bonus
    +82%
    Outputs a score from0 → 100

    Then adjusted by what you want

    2

    Preference Multiplier

    "Does this career match what the user actually wants?"

    "I want higher salary"

    Boosts careers paying more. Pay cuts get penalized hard — a big cut makes a career nearly invisible in results.

    "I want work-life balance"

    Penalizes 50+ hour cultures and shift work. Boosts predictable Mon–Fri roles. A 60-hour job loses almost half its score.

    "I want job security"

    Three signals combined: Is the field growing? Are there many openings? Is AI unlikely to replace it?

    "I want to escape physical labor"

    Desk and remote jobs get a massive 1.5× boost. Heavy physical jobs are nearly removed from results.

    "I want creative / meaningful work"

    Boosts jobs requiring creativity, design, or originality. Also boosts healthcare, education, and social services.

    How the multiplier scale works

    0.3×
    Kills score
    0.5×
    Hurts
    1.0×
    No change
    1.3×
    Boosts
    1.5×
    Supercharge

    Example: User wants "higher salary" + "escape physical labor"

    Logistics Coord. (+$18K, desk)
    1.4×
    Forklift Operator (+$4K, physical)
    0.6×
    Material Handler (−$2K, heavy)
    0.3×

    Risk tolerance adjusts the spread

    Play it safe
    Compress scores
    Swing for fences
    Amplify extremes
    Outputs a multiplier from0.3× → 1.8×

    Then diversity is enforced

    3

    Diversity Bonus

    "Are we showing enough hidden gems from different industries?"

    New sector? Score goes up.

    The first career from a brand-new industry gets a 1.25× boost. This is what surfaces surprising careers you'd never have googled.

    +25%

    Same sector again? Score goes down.

    If 3+ results are already in your current industry, the next one gets penalized. Stops the "bus driver → truck driver → train driver" problem.

    −15%

    Example: Building results for a Warehouse Worker

    Result #1: Logistics Coord.
    1.25×
    Result #2: Supply Chain Analyst
    1.12×
    Result #5: Another warehouse role
    0.85×
    Result #8: Yet another warehouse
    0.70×
    Outputs a multiplier from0.7× → 1.25×

    Scores combined, then safety checks

    Safety Checks

    After scoring, we run rules to catch algorithm failures before results reach the user.

    40% Diversity Floor

    At least 40% of top 15 results must be cross-sector. If not, promote hidden gems from further down.

    Salary Sanity Check

    If user said "higher salary," remove any results paying less (unless within 5%).

    Effort Reality Check

    Validate training hour estimates against real certification databases. Flag anything unrealistic.

    Smart Defaults

    Auto-set sort and filters based on priorities. "Swing for the fences" → sort by salary. "Play it safe" → sort by easiest.

    Final ranked results

    Your Ranked Career Matches

    Top 15–20 careers, ranked by PathScorer.
    Each tagged as cross-sector, adjacent, or same-sector.

    87
    Highest score
    40%+
    Cross-sector
    +50%
    Avg salary boost

    Example output: Warehouse Worker, "swing for the fences"

    1Logistics Coordinator87Cross
    2Supply Chain Analyst84Cross
    3E-Commerce Ops Specialist82Cross
    4Corporate Trainer79Cross
    5Procurement Specialist78Cross
    6Quality Assurance Inspector76Adjacent

    PathScorer Algorithm v1 · Data: O*NET 28.1 + BLS OES 2024 · 1000+ occupations scored per user

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