Which Roles Valor Ventures Portfolio Companies Posted

Methodology and data

Who is included

Companies listed as current portfolio companies of Valor Ventures on https://valor.vc/portfolio/.

This is a tracked sample, not a verified complete census. 26 companies are included; 11 have observed posting history and 19 have a headcount reading. A company without a found posting may still be hiring through other channels. Company shares use all 26 tracked companies as the denominator.

The next hire that raises revenue per employee is advice from fixed rules, not a finding (src/lib/leverage.ts): a pattern counts when a company posted at least 3 roles in it in the twelve months (five for software engineering), and a company that already posted a role covering the hire, such as revenue operations for a GTM engineer, is not listed under it.

Collection limit: the 20 most recent postings per company; 3 companies reached that limit. Counts describe observed postings, not unique vacancies or completed hires. Reposts can represent the same search, and limited histories cannot establish a complete company ranking or a hiring growth rate.

26 companies in the census; 19 with a headcount reading; job boards read for 0 (the live board read has not run for this panel yet, so open roles are unknown, not zero); 90 postings over the last twelve months for 11 of them, the 20 most recent a company (3 companies at that cap, so earlier quarters are undercounted); signals cover the last 180 days. Fact sheet computed 2026-10-14; study written 2026-10-14; dataset version vertical@1.0.0; intelligence rules intelligence@0.2.1; claim check: stripped (2 statements flagged by the checker). The executive summary, the sections, the FAQ answers and the limitations were revised by Human in the Loop Talent on 2026-10-14 against this fact sheet: The generated narrative described a share below a flat year as "accelerating", read unmapped leadership seats as settled, said two companies "added" leaders where the sheet shows two postings, and miscounted the companies above 100 employees. This revision corrects each against the same fact sheet (archive 2025-10-14 to 2026-10-14) and keeps the method to the limitations paragraph.

How the census and the article were made

Original research computed from the signal layer of the recruiting CRM behind Human in the Loop Talent: public job boards, company news, funding announcements and headcount readings, collected continuously for every tracked company and recomputed every Sunday. A figure is published only when the sample supports it; the data behind every page is published as JSON next to it.

1. The category is defined in one sentence (who is in, who is out). A search plan turns it into a structured company-database query (keywords, products, funding stages, founding year, countries, size band) and six to ten web-search angles.
2. Three feeds enumerate the companies: the structured query against a company database; the list pages a researcher would read for each angle, fetched whole and read for every company they name; and bulk firmographic enrichment (headcount, industry, headquarters) for any company that arrived without them. The lists merge by company domain, then by name.
3. Companies that pass the inclusion gate (category, size band, geography) become tracked companies in the CRM, so the signal layer watches the whole category: job postings every weekday, news, funding and leadership changes. The last twelve months of each company's postings are read from a job-postings archive, so the study sees a year of hiring, not only what is open today.
4. After at least a week of observation a fact sheet is computed in code: headcount bands, twelve months of hiring by role family and quarter, open roles, open leadership searches, tools named in job posts, revenue-seat changes, funding and AI initiatives over 180 days.
5. The study is written from the fact sheet only. A second pass checks every number, name, title and date against the sheet and strips anything it does not contain; the claim-check result is shown on the page.
6. A census under ten companies is not published. Studies are rewritten when the census is at least a week old and the last study is older than four weeks.
7. The company table is derived in code from the fact sheet by the rules stated under “How the table is built”; it is recomputed whenever the fact sheet or the rules change, and both versions are printed on the page.

How the table is built

• Strong: at least two independent evidence classes support the state (for example a leadership role in the posting archive and a role opened in the last sixty days of job-board reads, or a funding event), and the latest evidence is 90 days old or newer.
• Moderate: one evidence class with at least three corroborating postings for the state and evidence 90 days old or newer, or two classes whose latest evidence is older than 90 days.
• Weak: a single posting or event, or evidence older than 90 days without corroboration.
• Insufficient: no company-level evidence; the company is counted in the census but not listed.
• A dated leadership posting and the archive count that contains it are one class, not two: they describe the same posting. A posting from the last sixty days is likewise the archive's own newest row and counts with it, not as a second class; only a role-opened signal from the daily board reads, a company event or a mapped seat is independent evidence.
• Confidence is qualitative. No numeric probability is assigned because the states have not yet been calibrated against observed outcomes.
• Priority is commercial fit, kept separate from evidence strength: how closely the hypothesised need matches what Human in the Loop Talent recruits for (revenue leadership, first GTM hires, AI engineering and AI leadership, post-raise hiring plans).
• Act now: a seat-level or first-function need (revenue leadership, first sales function, AI leadership) with moderate or strong evidence that is 90 days old or newer, or an open leadership search.
• Watch: a team-building or post-funding state with moderate or strong evidence, or an act-now state with only weak or stale evidence.
• Monitor: everything else with at least one signal.
• Freshness is measured from the latest dated evidence to the date the fact sheet was computed, never to the day the page was built.
• Fresh: 30 days or newer. Recent: 90 days or newer. Stale: older than 90 days. Undated: the archive gives only twelve-month counts for this company.
• Thresholds scale with headcount. Up to 50 employees: 3 GTM or engineering postings in the year count as a build, 5 postings in 90 days as high velocity. Up to 200: 5 and 8. Up to 1,000: 8 and 15. Above 1,000: 12 and 25.
• A company at the archive cap whose cap is below the velocity threshold for its size cannot be measured for velocity, and no velocity signal is produced for it.
• Where the fact sheet does not carry a company's own headcount, the panel median stands in and the row says so.
• Rows are ordered by priority (act now, watch, monitor), then evidence strength, then freshness, then number of distinct signal types, then company name.
• Counts for a company at the archive cap are floors, not totals; they never raise a company above one with the same evidence profile.
• The ordering is a function of the published JSON and this rules version, so it can be re-derived from the download.

What would confirm or disprove this reading

Each state above is a hypothesis about an organisation, read from what it posted. The panel can test it as the weekly refresh adds observations; the reader can test it faster by checking the company directly.

• Building or replacing revenue leadership (2 companies). Alternative explanations: A backfill for a leader who left, not a new seat. Title inflation: a "Head of Sales" at a very small company may be the first account executive. An evergreen or reposted listing rather than a live search. A duplicate carried by a job aggregator. Would confirm: The posting is live on the company's own careers page. No sitting revenue leader on the company's LinkedIn page, or one whose tenure ended recently. Account executive or SDR postings following within a quarter. Would disprove: A revenue leader in seat whose tenure predates the posting. The posting withdrawn without a hire announcement.
• Building a first sales function (6 companies). Alternative explanations: Backfilling attrition in a sales team that already exists. A single account executive alongside continued founder-led sales. Outsourced or contract SDR capacity posted under the company name. A leadership seat that exists but was never posted publicly. Would confirm: No head of sales or VP Sales on the company's LinkedIn page. The founder or CEO owning sales in the posting or on LinkedIn. A sales leadership posting following the IC hires. Would disprove: A sitting sales leader with tenure longer than a year. The postings are replacements for named departures.
• Scaling the go-to-market team (6 companies). Alternative explanations: Churn backfill in a large existing team rather than growth. Several postings for one seat (locations, reposts, agency duplicates). A company at the archive cap: the count is a floor and the mix is skewed to its newest postings. Would confirm: Headcount in sales and customer roles rising on LinkedIn over two readings. New territories or segments named in the postings. A revenue operations or enablement posting alongside. Would disprove: Flat GTM headcount across readings. Postings reappearing with the same title every quarter.
• Hiring at high velocity for its size (2 companies). Alternative explanations: Seasonal or batch posting after a planning cycle. Aggregator duplicates inflating the count. A company at the archive cap shows only its newest postings, which makes every posting look recent. Would confirm: Distinct titles and teams across the postings. Headcount rising across readings. Would disprove: The same handful of titles reposted. Headcount flat or falling.
• Engineering and product build-out (3 companies). Alternative explanations: Steady-state backfill in a large engineering organisation. One role posted across several locations. Would confirm: Engineering headcount rising across readings. A new product line or platform named in the postings. Would disprove: Flat engineering headcount.
• Across the panel. Headcount readings two refreshes apart would show whether the companies hiring fastest grew; a live read of the job boards would turn the posting archive into open-role counts; mapped leadership seats would turn “no CRO change recorded” into an observation.

Key figures

The highest recent share among companies below the collection limit is Account executive at 18% of its 11 postings. This describes timing in the observed subset; it does not establish a portfolio-wide increase or decrease in hiring.

Postings by quarter are not shown: with 3 companies at the archive cap, earlier quarters are undercounted and a quarterly series would read as growth that is an artefact of the cap.

Limitations

This study covers the 26 companies listed as current Valor Ventures portfolio companies, 19 of which have a headcount reading. The study reads a twelve-month archive of job postings, not live job boards, so it reports what was posted and when rather than which roles are open today. The hiring figures come from a twelve-month archive of postings (2025-10-14 to 2026-10-14) covering 11 companies and 90 postings, capped at the 20 most recent per company; Arpio, FirmPilot and The Gathering Spot sit at that cap, so their counts are floors. The other 15 companies have no posting in the archive and are unknown, not idle. The sixty-day figures are posting dates from that archive and the daily board reads, not a read of what is open. Leadership seat holders are not mapped and company events have not been collected for this panel, so nothing is known about CRO changes or funding either way. Months named in the text are the dates the postings appeared in this archive.

• 15 of 26 companies have no postings in the twelve-month archive. That is unknown, not idle: the archive covers public job boards and does not reach every company.
• The archive keeps the 20 most recent postings per company and 3 of 11 companies with postings sit at that cap. Their counts are floors, their older quarters are undercounted, and a quarter-by-quarter trend cannot be read from this panel.
• This fact sheet does not carry each company's headcount, so size-scaled thresholds use the panel median (23 employees) for every company. Rows will sharpen when the panel is recomputed with per-company headcount.
• Open roles have not been read live from these companies' job boards, so no row states a current open-role count. "Posted in the last 60 days" (18 roles at 5 companies) is a posting date in the archive or the daily board reads, not a live read of the board.
• Leadership seat holders are not mapped for this panel. Any statement that no CRO changed means none was recorded, not that none happened.
• Company-event signals (funding, acquisitions, AI initiatives, stack changes) have not been collected for this panel yet. Zero means not collected.
• 19 of 26 companies have a headcount reading; the rest are counted in the census without a size.
• The company table is a set of hypotheses, not findings. Its confidence labels are qualitative and uncalibrated; its priority column is the practice's commercial view and should be read as such.
• Nothing on this page states that a hire caused growth or that a company will hire; the panel records correlations and the page says which ones.

Dataset and versions

• Fact sheet and study: /research/verticals/valor-ventures-portfolio.json (version vertical@1.0.0, computed 2026-10-14).
• Company opportunity table: rendered on this page by rules intelligence@0.2.1; not offered as a separate download until redistribution terms for the underlying posting archive are confirmed.
• Revision log: 3 revisions since 2026-10-12, read from the git history of the published JSON.
• Licence: CC BY 4.0. Reuse with attribution.

Cite this

Related: all intelligence, which roles precede growth, startup recruiting, post-funding hiring.