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Solmaz Shahalizadeh

AI Operating Partner, HGGC · 1 appearance on the record.

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

investorexecutivescientistoperator

Solmaz Shahalizadeh is an AI Operating Partner at HGGC and a Founding Partner at Backbone Angels. She previously spent nine years at Shopify as VP and Head of Data Science & Engineering, where she scaled the data team and developed AI products including Shopify Capital.

16statements → 8claims → 3claims resolved → 67%fully supported → 4/5average certainty → 1.62/5average debate potential → 4.3/5argument clarity · the sources →

2 supported 1 partly supported 0 contradicted 5 not checkable as stated how the 8 claims stand · each chip opens the sources

1 prediction · 7 assertions · 2 insights · 6 disclosures · every statement was checked. The prediction and assertions are the 8 claims: statements the public record can support or contradict. 3 are resolved, and 5 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 Solmaz 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
Shahalizadeh: Shopify supports over 800,000 merchants across 175+ countries
“Last reported number, sort of, is that we are over 800,000 merchants in over a 175 countries.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 0:28 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
75% certainty 4
100% 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

Argument clarity: do they answer the question? how? →

4.3 / 5 directness 4.8 · coherence 4.6 · precision 4.2 · compression 3.8

answered every one of 9 assessed questions directly

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

280 words/min while actually speaking · 41.5 um and uh per 1k words

Measured by listening to the audio itself: 5,399 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 Solmaz Shahalizadeh said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Prediction Not checkable as stated
Causal inference will solve key data problems ignored by machine learning hype
“There's a lot of hype and focus and good work in machine learning. But I do think if we take a step back and look at causal inference, we're gonna actually solve a lot of problems with just that arm. So I hope over the next few years, like, people invest in th…”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 26:14 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Insight
Shahalizadeh: Moving from rules to machine learning yields highest lift
“The first time you go from any rule base or non-machine learning to machine learning actually is the time that you get the highest lift.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 14:02 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shahalizadeh hires machine learning talent by looking outside traditional ML backgrounds
“I recruit machine learning people by not going after machine learning people.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 22:55 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Insight
Shahalizadeh: Answering simple questions drives more data impact than machine learning
“And I think that's sort of a neglected part of data science. Everyone talks about, like machine learning and building all of these data products, but I think lots of the impact, lots of the trust and usage of data actually comes from being able to answer to so…”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 31:36 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shahalizadeh: Shopify avoids asking merchants for data without immediate utility
“We don't ask merchants for data that we are not going to give a benefit to them right off the bat.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 19:07 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shahalizadeh: Shopify incurred technical debt by jumping straight to ML
“And there has been times that even us, we've sort of, like, jumped to the top top in one of the areas, and you pay the technical debt that you did not do the work in the beginning.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 21:22 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Not checkable as stated
Shahalizadeh: Shopify ML models are built for individual merchant use cases
“So actually all of our machine learning offerings that I was talking about are actually built for the individual use case of the merchant.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 29:03 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Supported
Shahalizadeh: Shopify supports over 800,000 merchants across 175+ countries
“Last reported number, sort of, is that we are over 800,000 merchants in over a 175 countries.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 0:28 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Partly supported
Shahalizadeh: Shopify expanded from 300 to over 5,000 employees
“When I joined, we were 300 people, and now we are over 5000, so it's been massive growth, really, over the years.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 1:17 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Not checkable as stated
Shahalizadeh: Compute advances reduced two-year neural net tasks to one week
“What took me two years along with a team to do, like, right now with the advances over the last few years in compute and in storage, you can do probably over a week.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 3:31 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shopify transitioned its ETL platform from Ruby to Python around 2013-2014
“Shopify, the application is still Ruby on Rails, and actually the first version of ETL tooling that was built in house was Ruby, but I think we made this decision of moving to Python around 20 13, 20 14, because we thought it's the language that most of the co…”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 9:09 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Not checkable as stated
Shopify Capital is entirely driven by machine learning
“As of last two years, Shopify Capital is entirely machine learning driven.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 12:03 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Not checkable as stated
Shahalizadeh: Shopify processes over 600,000 checkouts during peak periods
“At peak time we had over 600,000 checkouts going through”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 13:46 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Assertion Supported
Shahalizadeh: Shopify admin dashboard home cards are driven by machine learning
“When they log into their Shopify store in the admin, they see a bunch of, like, home cards that tells them do this or, like market for this product or make this change in your theme. And those are all driven by machine learning,”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 18:23 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shopify shadow-tests machine learning models using Apache Kafka before release
“We run them in shadow mode, which means, like, they do all the evaluation, but the results are logged to Kafka, and we see if the time to response, if the sort of distribution of predictions are different or not.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 28:05 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)
Disclosure
Shopify uses Facebook's Prophet for automated metric forecasting and anomaly detection
“We use profit for time series forecasting all the time in Shopify, and it has changed The way teams work from having people in operations monitor metrics and see what changed to this is an anomaly.”
Solmaz Shahalizadeh Jun 12, 2019 ▶ 33:25 Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driven NYC)

Appearances (1)

EpisodeDateSpeaking time
Fireside Chat: Solmaz Shahalizadeh, VP of Data Science & Engineering at Shopify (Data Driv Jun 12, 2019 23m
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