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Kieran Snyder

VP of Product, AI, Microsoft · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

founderexecutiveacademicauthor@KieranSnyder ↗LinkedIn ↗nerdprocessor.com ↗

After product leadership stints at Microsoft and Amazon, Snyder co-founded Textio in 2014, serving as CEO for nearly a decade while developing AI-driven writing and bias-detection tools. Holding a PhD in linguistics, she conducts empirical research on workplace gender bias and publishes data-driven analyses through her newsletter nerd processor.

9statements → 5claims → 2claims resolved → 3.78/5average certainty → 1.78/5average debate potential →

1 supported 1 partly supported 0 contradicted 3 not checkable as stated how the 5 claims stand · each chip opens the sources

5 assertions · 3 insights · 1 disclosure · every statement was checked. The predictions and assertions are the 5 claims: statements the public record can support or contradict. 2 are resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Kieran argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Corporate jargon in job postings disproportionately deters applicants of color
“Like some of that corporate jargon, the words like synergy and stakeholders and ROI. It turns out everybody hates them, but underrepresented groups, especially people of color, hate them even more. So nobody is as likely to apply when you include that language…”
Kieran Snyder Mar 18, 2016 ▶ 17:37 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
50% certainty 3
100% certainty 4
none yet certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

232 words/min while actually speaking · 23.1 um and uh per 1k words

No argument clarity score for Kieran Snyder: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 4,065 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Kieran Snyder said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Not checkable as stated
Job listings with over 50% bullet points significantly reduce female applicants
“If you go above 50% bulleted content in a listing, you quickly reduce the proportion of women who are likely to apply for the job, statistically.”
Kieran Snyder Mar 18, 2016 ▶ 12:09 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Insight
Diversity of an applicant pool strongly predicts how quickly jobs fill
“It turns out that the percentage of underrepresented groups who apply to a job is a very good predictor of how quickly the role will fill”
Kieran Snyder Mar 18, 2016 ▶ 9:07 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Assertion Supported
Corporate jargon in job postings disproportionately deters applicants of color
“Like some of that corporate jargon, the words like synergy and stakeholders and ROI. It turns out everybody hates them, but underrepresented groups, especially people of color, hate them even more. So nobody is as likely to apply when you include that language…”
Kieran Snyder Mar 18, 2016 ▶ 17:37 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Insight
Machine learning models often predict outcomes without explaining why they happen
“The models that you can create can often tell you what might happen, but they can't always tell you why.”
Kieran Snyder Mar 18, 2016 ▶ 1:04 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Insight
Text is the primary daily volume output of almost every business
“Whatever business you are in, whether you're making software, or you're making hamburgers, or you're making playground equipment, The thing you're actually making the most of every day at work is text, ah, almost certainly.”
Kieran Snyder Mar 18, 2016 ▶ 1:18 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Assertion Not checkable as stated
Over a third of recruited candidates abandon applications after reading the listing
“Over a third of people that you're reaching out to who are looking for jobs walk away when they see your job listing.”
Kieran Snyder Mar 18, 2016 ▶ 3:16 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Assertion Not checkable as stated
Job seekers scan listings for only a couple seconds before deciding
“Turns out people scan a listing for only a couple seconds before deciding whether to engage.”
Kieran Snyder Mar 18, 2016 ▶ 3:07 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Disclosure
Textio collected 15 million job listings tagged with performance outcome metrics
“At this point we have about fifteen million job listings across industries and geographies that are tagged with, in many cases, very rich outcomes.”
Kieran Snyder Mar 18, 2016 ▶ 4:39 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
Assertion Partly supported
The average corporate cost per hire is approximately $2,000
“Considering your average cost per hire is about 2000 dollars, you're saving a fairly substantial amount”
Kieran Snyder Mar 18, 2016 ▶ 17:03 Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)

Appearances (1)

EpisodeDateSpeaking time
Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / Fi Mar 18, 2016 20m
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