Methodology
TalentFlow ranks companies by where skilled people appear to move across company histories. We don’t survey anyone or use opinions; the ranking is computed from objective, company-to-company movement evidence drawn from publicly available professional histories. This page explains exactly how, including what the numbers can and can’t tell you.
1. Where the data comes from
For each ordered company pair A → B, we query public professional-profile search data for profiles whose current company is B and whose prior work history lists A. We use the aggregate result count only. TalentFlow is not a directory of individuals and does not publish personal profiles.
2. The talent-flow graph
The graph is directed: an edge A → B means there is movement evidence from A into B. In production this is not guaranteed to be an immediate job-to-job transition. It means public profiles currently matching B also list A somewhere in their prior work history.
3. Movement weights
The raw pairwise count is c(A,B): the number of profile-search results currently matching B and listing A in past experience. These are pairwise company-history matches, not unique people. One person can contribute to multiple source edges if they have worked at multiple companies. The company ranking uses these objective movement-matrix counts without time decay.
4. Prestige score
We first compute a SpringRank prior on the raw movement graph. SpringRank estimates which companies are net importers of talent from strong companies while accounting for the full directed graph rather than simple volume.
For the final score, we convert pairwise counts into positive net movement weights:
w(A,B) = max(c(A,B) - c(B,A), 0)
Then each company’s score is a weighted average of selected source-company priors:
score(B) = Sum(w(A,B) x SpringRank(A)) / Sum(w(A,B))
where A is included when rank(A) <= rank(B) + sqrt(N) or w(A,B) / total_inflow(B) >= 1 / N^(2/3). Here N is the number of ranked companies.
- A company gains prestige when talent flows into it from strong source companies.
- Quality matters more than volume. Large inflows from weak sources still count, but they pull the weighted average down rather than inflating the score.
- Small source flows are included only when they are rank-realistic, which preserves meaningful elite signals without letting tiny artifacts dominate the ranking.
5. Evidence floor and tiers
A company must clear a minimum amount of positive net inbound movement evidence before it can earn a ranked tier. This stops a company from jumping to the top off a single lucky data point. Companies that clear the floor are placed on a 0-100 percentile by prestige score and bucketed into tiers by rank: S for the top 10, A for the next 20, B for the next 30, C for the next 50, D for the next 80, and E for the rest.
6. Other signals we show
Alongside the prestige score, each company page shows descriptive flow signals computed from the same movement matrix: inbound movement evidence, outbound movement evidence, net movement evidence, and the top connected companies. These are pairwise company-history counts, not unique employee counts, and not guaranteed immediate transitions. They are meant to be read as “where this company’s talent connects,” not as judgments.
7. Coverage and unlocking
Rankings compare companies within the published movement-matrix coverage. Companies that appear only as sources or destinations can exist in the graph before they are published as full company pages. Unlocking a company adds coverage and lets future rebuilds incorporate more evidence, after which its graph and rank are published for everyone.
8. TalentFlow score for people
The people score applies company prestige signals to a single career history. Each role gets the prestige percentile of its company, then that role is weighted by tenure with exponential time decay. Each month worked contributes weight, and recent months count more than older months.
In formula form: career prestige = Sum(company prestige x role weight) / Sum(role weight).
The final people score also rewards accumulated matched experience: TalentFlow Score = career prestige x sqrt(total role weight / (total role weight + 1)). Current company, trajectory, and confidence are shown as supporting signals, but they do not change the score itself.
For a role from start date s to end date e, role weight = Integral from s to e of 2^(-age(t) / H) dt, where H is the profile-score half-life. Current roles use today as the end date.
Unknown companies are not treated as zero. If a company in the profile has no usable signal, it is excluded from the score and lowers the confidence label instead. This avoids punishing people for startups, small firms, international companies, or employers that TalentFlow has not ranked yet.
9. Limitations
We’d rather be honest about what this is than oversell it:
- It’s a statistical estimate from a sample of public data, not a complete census, and not a statement of fact. Profiles can be incomplete, outdated, or inaccurate.
- Coverage affects the numbers.While we’re still building out the dataset, companies inside the movement matrix have much better evidence than companies outside it. Rankings become more reliable as coverage across companies expands, and we actively avoid treating raw headcount as prestige.
- Rankings compare companies within the set we’ve published. A company’s tier describes its standing among ranked companies, which changes as more are added.
- We measure company-to-company movement. Firms whose prestige comes mainly from hiring top graduates straight from university and training them internally are currently under-represented, because a first job out of school has no prior company to flow from. We plan to incorporate education data to capture this.
- Do not use this to make decisions about individuals. It is company-level, aggregate, and informational only.
10. Corrections and removal
If something looks wrong, or you represent a company or individual and want data corrected or removed, see our Privacy Policy for how to reach us. We act on reasonable requests.