StarZero All shows Try the podcast clipper →

Mar 20, 2014 · 25m · mad

Jake Klamka, Insight Data Science // Data Driven #25 (Hosted by FirstMark Capital)

Jake Klamka · 19m spoken Matt Turck · 2m spoken Michael Kutai · 34s spoken Ray Prisman · 22s spoken Courtney Benson · 10s spoken
0:00 / 0:00
▶ Watch on YouTube →

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At Data Driven NYC, Insight Data Science founder Jake Klamka discusses how quantitative PhDs can transition from academia into tech industry data science roles. He details the structure of the Insight Fellows Program, outlines the core categories of data science work, and provides actionable advice for hiring managers seeking top data talent.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 11.9% of the talking time here. How this is scored →

Matt as informed peer 0.8 Guest teaching 1.0 Guest disagreement 0.3 Matt pushing back 0.5
05100:0010:0020:000:00–4:27 · Matt as informed peer 1/10 Welcome and Speaker Introductions by Host Matt Turck Matt Turck opens the meetup by welcoming attendees, mentioning Bloomberg as host, and outlining the speaker schedule. Jake Klamka then introduces himself and his transition from particle physics at the Large Hadron Collider to Silicon Valley startups.4:27–8:23 · Matt as informed peer 0/10 Overview of Insight Data Science Fellows Program In this guest monologue, Jake details the structure of the six-week Insight Data Science fellowship, its high placement rate, and how the fellows network builds long-term industry connections. Host-side engagement is absent during this presentation segment.8:23–17:04 · Matt as informed peer 0/10 Data Science Role Categories and Bridging the Gap Jake presents a breakdown of data science subfields (product analytics, predictive modeling, data products) and offers hiring advice for companies transitioning PhDs into industry roles. Because this is a continuous slide presentation without host interaction, host scores remain zero.17:04–25:20 · Matt as informed peer 2/10 Key Takeaways and Launching Insight in New York Matt moderates a Q&A session, questioning whether several extra years for a PhD truly yield better data science candidates than a Master's degree. Jake explains the problem-formulation mindset PhDs develop, while audience members join in with questions about corporate hiring mistakes.0:00–4:27 · Guest teaching 0/10 Welcome and Speaker Introductions by Host Matt Turck Matt Turck opens the meetup by welcoming attendees, mentioning Bloomberg as host, and outlining the speaker schedule. Jake Klamka then introduces himself and his transition from particle physics at the Large Hadron Collider to Silicon Valley startups.4:27–8:23 · Guest teaching 1/10 Overview of Insight Data Science Fellows Program In this guest monologue, Jake details the structure of the six-week Insight Data Science fellowship, its high placement rate, and how the fellows network builds long-term industry connections. Host-side engagement is absent during this presentation segment.8:23–17:04 · Guest teaching 1/10 Data Science Role Categories and Bridging the Gap Jake presents a breakdown of data science subfields (product analytics, predictive modeling, data products) and offers hiring advice for companies transitioning PhDs into industry roles. Because this is a continuous slide presentation without host interaction, host scores remain zero.17:04–25:20 · Guest teaching 2/10 Key Takeaways and Launching Insight in New York Matt moderates a Q&A session, questioning whether several extra years for a PhD truly yield better data science candidates than a Master's degree. Jake explains the problem-formulation mindset PhDs develop, while audience members join in with questions about corporate hiring mistakes.0:00–4:27 · Guest disagreement 0/10 Welcome and Speaker Introductions by Host Matt Turck Matt Turck opens the meetup by welcoming attendees, mentioning Bloomberg as host, and outlining the speaker schedule. Jake Klamka then introduces himself and his transition from particle physics at the Large Hadron Collider to Silicon Valley startups.4:27–8:23 · Guest disagreement 0/10 Overview of Insight Data Science Fellows Program In this guest monologue, Jake details the structure of the six-week Insight Data Science fellowship, its high placement rate, and how the fellows network builds long-term industry connections. Host-side engagement is absent during this presentation segment.8:23–17:04 · Guest disagreement 0/10 Data Science Role Categories and Bridging the Gap Jake presents a breakdown of data science subfields (product analytics, predictive modeling, data products) and offers hiring advice for companies transitioning PhDs into industry roles. Because this is a continuous slide presentation without host interaction, host scores remain zero.17:04–25:20 · Guest disagreement 1/10 Key Takeaways and Launching Insight in New York Matt moderates a Q&A session, questioning whether several extra years for a PhD truly yield better data science candidates than a Master's degree. Jake explains the problem-formulation mindset PhDs develop, while audience members join in with questions about corporate hiring mistakes.0:00–4:27 · Matt pushing back 0/10 Welcome and Speaker Introductions by Host Matt Turck Matt Turck opens the meetup by welcoming attendees, mentioning Bloomberg as host, and outlining the speaker schedule. Jake Klamka then introduces himself and his transition from particle physics at the Large Hadron Collider to Silicon Valley startups.4:27–8:23 · Matt pushing back 0/10 Overview of Insight Data Science Fellows Program In this guest monologue, Jake details the structure of the six-week Insight Data Science fellowship, its high placement rate, and how the fellows network builds long-term industry connections. Host-side engagement is absent during this presentation segment.8:23–17:04 · Matt pushing back 0/10 Data Science Role Categories and Bridging the Gap Jake presents a breakdown of data science subfields (product analytics, predictive modeling, data products) and offers hiring advice for companies transitioning PhDs into industry roles. Because this is a continuous slide presentation without host interaction, host scores remain zero.17:04–25:20 · Matt pushing back 2/10 Key Takeaways and Launching Insight in New York Matt moderates a Q&A session, questioning whether several extra years for a PhD truly yield better data science candidates than a Master's degree. Jake explains the problem-formulation mindset PhDs develop, while audience members join in with questions about corporate hiring mistakes.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 55.7% · guest 44.3%0:00 · Matt 55.7% · guest 44.3%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%15:00 · Matt 0% · guest 100%15:00 · Matt 0% · guest 100%18:00 · Matt 25.6% · guest 74.4%18:00 · Matt 25.6% · guest 74.4%21:00 · Matt 15.9% · guest 84.1%21:00 · Matt 15.9% · guest 84.1%24:00 · Matt 10.1% · guest 89.9%24:00 · Matt 10.1% · guest 89.9%
Sharpest disagreement ▶ 23:38 Audience member Ray Prisman pushes back on Jake's advice

Ray Prisman explicitly tells Jake he wants to push back on the idea that companies are failing by not specifying precise data science roles, arguing that defining their own value is part of a data scientist's job.

 Hardest push from Matt ▶ 18:38 Matt challenges the added value of a full PhD program

Matt politely presses Jake on whether spending three additional years completing a PhD actually creates a better data scientist than hiring someone with a Master's degree.

 Biggest teaching moment ▶ 19:00 Jake clarifies why PhD struggle builds question-asking abilities

Jake explains that unlike Master's programs focused on coursework, PhD research forces candidates to discover insights in messy open-ended data where questions aren't pre-formulated.

 Matt holds his own ▶ 21:21 Matt cites prior speaker Drew Conway on social science PhDs

Matt demonstrates domain context by bringing up a past presentation by Drew Conway regarding social science PhDs entering data science to complement an audience question.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Welcome and Speaker Introductions by Host Matt Turck 1000 Matt Turck opens the meetup by welcoming attendees, mentioning Bloomberg as host, and outlining the speaker schedule. Jake Klamka then introduces himself and his transition from particle physics at the Large Hadron Collider to Silicon Valley startups.
Overview of Insight Data Science Fellows Program 0100 In this guest monologue, Jake details the structure of the six-week Insight Data Science fellowship, its high placement rate, and how the fellows network builds long-term industry connections. Host-side engagement is absent during this presentation segment.
Data Science Role Categories and Bridging the Gap 0100 Jake presents a breakdown of data science subfields (product analytics, predictive modeling, data products) and offers hiring advice for companies transitioning PhDs into industry roles. Because this is a continuous slide presentation without host interaction, host scores remain zero.
Key Takeaways and Launching Insight in New York 2212 Matt moderates a Q&A session, questioning whether several extra years for a PhD truly yield better data science candidates than a Master's degree. Jake explains the problem-formulation mindset PhDs develop, while audience members join in with questions about corporate hiring mistakes.

Statements from this episode (6)

Assertion Not checkable as stated
Turck: Knewton likely has New York's largest data science team
“And then we have Jesse from Newton, which is one of the first mark portfolio companies, in full disclosure who I think runs The largest data science team in New York, as far as I know.”
Matt Turck Mar 20, 2014 ▶ 0:54
Assertion Partly supported
Klamka: PhD graduates increase while academic faculty positions stay flat
“The amount of PhDs, granted, keeps going up, but the amount of faculty positions has been flat.”
Jake Klamka Mar 20, 2014 ▶ 3:51
Insight
Industry data scientists must aim for 80% perfection, unlike in academia
“Your goal is to go get everything a hundred percent right and then publish, and if that takes a couple of years, then you take a couple years. Ah, in our industry, in, you know, in industry, clearly that's not gonna work. Ah, you wanna probably get to, like, 8…”
Jake Klamka Mar 20, 2014 ▶ 15:22
Insight
Test data science candidates on learning velocity instead of static skills
“Adjust your, if at all possible, try to think of ways to adjust your hiring process to test for how quickly they're able to kind of traverse this gap. Kind of get two data points basically and see how, how quickly they're moving along instead of just kind of t…”
Jake Klamka Mar 20, 2014 ▶ 16:47
Insight
PhDs outperform master's graduates in data science because of research experience
“From what I've seen, yes. And I'm, clearly I'm biased, because I'm working with a bunch of PhDs, but I've also, but in speaking with a lot of hiring managers, the answer I've heard is yes. And the reason is the, that underlying skill set of, in data science, y…”
Jake Klamka Mar 20, 2014 ▶ 19:01
Insight
The biggest mistake companies make is interviewing data scientists like software engineers
“Great question. And so, again, this is biased predominantly towards sort of tech companies, but I think the biggest one is actually interviewing them as if they were software engineers. And so, in particular, the fellows, when they graduate, the program go int…”
Jake Klamka Mar 20, 2014 ▶ 21:58
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.

Try the podcast clipper Learn more