Dec 5, 2013 · 21m · mad
Hilary Mason // Data Driven NYC 20 // Nov 2013
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
At a Data Driven NYC event, data scientist Hilary Mason delivers a structured presentation on organizational data science, offering practical frameworks for team building, project prioritization, and analysis before engaging in an audience Q&A.
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 2.8% of the talking time here. How this is scored →
Matt as informed peer 0.0 Guest teaching 6.5 Guest disagreement 0.8 Matt pushing back 0.0
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 17:07 Firm rejection of opaque algorithms
When asked if using an unexplainable algorithm is acceptable, Hilary flatly rejects the premise, stating that not understanding system behavior leads to severe problems in startup environments.
Hardest push from Matt ▶ 17:57 Host enforcing Q&A decorumMatt Turck briefly interrupts an audience questioner to enforce event rules, asking him to state his name and company before asking his question.
Biggest teaching moment ▶ 11:55 Framework for prioritizing data initiativesHilary educates the audience on managing data science teams by asking how an evil dictator might use a feature to stretch creative problem solving outside product boundaries.
Matt holds his own ▶ 14:19 Host facilitating seamless audience transitionMatt Turck efficiently transitions from the presentation to audience Q&A, taking control of the room logistics.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Defining Companies and Data Science Infrastructure | 0 | 6 | 0 | 0 | Hilary delivers a presentation outlining data science roles and defining the data science team dynamic as a superhero ensemble of varied skills. The host does not speak during this monologue segment, requiring zero host expertise or pushback scores. | |
| Core Responsibilities of Data Science Roles | 0 | 6 | 0 | 0 | Hilary breaks down core responsibilities of data teams and categorizes project analysis into immediate needs, well-understood metrics, and speculative future-building. The segment is entirely a presentation monologue without host intervention. | |
| Five-Question Framework for Project Prioritization | 0 | 7 | 1 | 0 | Hilary shares a structured four-question framework for project prioritization and introduces the thought exercise of asking what an evil dictator would do with the product. As a monologue presentation, host metrics remain at zero. | |
| Data Gotham Conference Announcement and Conclusion | 0 | 7 | 2 | 0 | Matt Turck acts strictly as an event moderator taking audience Q&A while Hilary answers questions about unexplainable algorithms, team structures, and data democratization. Hilary shows slight pushback to an audience premise when she firmly rejects unexplainable black-box models. |
Statements from this episode (14)
Mason: Complex corporate data problems are either technical or organizational
“Mostly I just sort of talk to companies that have interesting or hard data problems, and those data problems tend to take two forms. They tend to be either highly technical You know, hours of whiteboarding infrastructure and coming up with ideas and that's rea…”
Mason: A company is infrastructure deployed to scale beyond oneself
“A company is a piece of infrastructure that you can deploy when you need to scale something beyond yourself.”
Hilary Mason: Data science deserves its own title combining math, code, and communication
“Data scientists as a job does deserve its own job title because these three things in one professional is new.”
Mason: The vast majority of math in data science is quite simple
“So at least in my practice of data science, the vast majority of the math we did was quite simple.”
Mason: Data engineering designs systems dependent on flowing data
“Data infrastructure. Or data engineering, which is the engineering of systems where the design of the system itself is dependent on the nature of the data that flows through it”
Hilary Mason: Bitly derived top data questions from support teams
“At Bitly we got some of our best questions through our tech support and our community team.”
Hilary Mason: Data analysts should investigate areas where intuition fails
“Generally, look for places where intuition fails. That is, ah, your company, your product, your data, you should have a good sense of what you would expect. And yet, when you see something in it that doesn't meet your expectations, ah, that's probably an indic…”
Mason: Forcing teams to admit informal error metrics motivates analytical rigor
“And if there are no error metrics that we are planning to use initially you know, we might be working on something like a recommendation algorithm. And the error metric for the first version is, it looks good to me. You at least have to admit that in public, i…”
Mason: Data projects must prove immediate relevance to business goals
“Assuming we can solve this perfectly, what is the first thing we'll do with it that makes sure it has immediate relevance to the product, or the business, or the system you're building? Because there are a lot of super cool ideas that do not tie directly to th…”
Mason: Asking how an evil dictator would use product sparks creative ideas
“So if you ask this question, like, if we put this thing in the hands of an evil dictator, ah, what would they do with it? Ah, it's a way to get people to sort of brainstorm and come up with crazy ideas for it, some of which might actually be good.”
Mason balances data teams with one predictable and one speculative project
“And in terms of the day-to-day management, I'd like to make sure everyone who I work with has both, you know, one sort of longer-term, more well-understood problem, and then one problem they can work on when they're really excited, ah, that's a little bit, as …”
Hilary Mason: Startups must understand how their machine learning models work
“Won't speak for the large companies or people who are working on very specific problems, but when you're generally building systems that in a startup environment, it's really important to understand why your system is doing the thing it's doing, or else it's g…”
Hilary Mason: Separating R&D from product rarely works for tech companies
“I have seen companies that successfully separate, ah, research and development as long as everyone still eats lunch together but generally that doesn't seem to be a great approach in that you eventually end up with a research department off in the corner publi…”
Mason: Relying on peers for data hurts efficiency; build self-serve dashboards
“Whenever somebody has to ask someone else for a piece of information to get their job done, it really makes everybody much less effective. So if you see people asking for the same kinds of information, there should be dashboards or a way that they can access i…”