S1: Science and Analytics Supporting Physical Production

Episode Summary

Manager of Studio Growth Science and Analytics, Jen Walraven, discusses using data to support the operational and logistical side of production, and how this supports content creators at Netflix in bringing their ideas to life around the world.

Episode Notes

Manager of Studio Growth Science and Analytics, Jen Walraven, discusses using data to support the operational and logistical side of production, and how this supports content creators at Netflix in bringing their ideas to life around the world.

Episode Transcription



Lyle:                Netflix is a big believer in data-driven analysis and understanding.  We use science and analytics to figure out what audience members like, and then suggest movies and shows we know they’ll really enjoy.  We’re also using science and analytics to improve our own internal processes in the production of our originals.  We use data-driven insight to make more cost-effective and timely productions.  I’m Lyle Troxell.  And on this episode of We Are Netflix, my cohost Michael Paulson and I speak with Manager of Studio Growth in Science and Analytics, Jen Walraven, in our Los Angeles Office.  Jen Walraven has a Bachelor of Arts in Computer Science from the University of California at Berkeley, along with a robust career in data and analytics at Deloitte, Slalom Consulting, and Nomis Solutions.  Jen is a yoga teacher.  Jen joined Netflix in 2017 as a Senior Analytics Engineer and is now a Manager of Science and Analytics located in our Los Angeles office.  Why do we need to have engineering down in Los Angeles area?Why does it need to be close to content creation?


Jen:                  The short answer is because our stakeholders benefit from it.  The longer answer is that particularly for the studio side, right, the original content production, which is what my team supports, it is very much the creative heart of the business.  But if you look—pardon the pun—but if you look behind the scenes, it’s very much like a supply chain.  And so there are a lot of areas where having access to data and tools and insight is hugely beneficial, especially when we think about how much content we’re producing around the world.  So being able to sit there, understand how that part of the business functions, and really kind of embed ourselves in with those teams means that we can make tools that are more useful to them. 


Lyle:                Well, how connected are you to production then?


Jen:                  The stakeholders that we support are the physical production team. So I’m not, you know, on set every day.  But people on my team have been to different productions.  And they’ve shadowed, you know, the assistant directors and the line producers, and learned a lot about what does it actually mean to work in entertainment.We’re supporting all of the physical production org.  So our VP of Visual Effects and his team, VP of Post Production and his team, the creative executives.  We’re really very closely involved with that part of Netflix.


Lyle:                I can imagine having a desire to have tools that make that whole workflow more efficient.  One of my first university jobs was to make workflow more efficient at the university.  So like looking at the business and figuring that out and then making tools and flows that were simpler and better and more efficient.  Is that kind of what you’re talking about?


Jen:                  Yeah, it is.  You know, I think there are sort of like layers to what we do.  Right?  The first one is just how do we help people do their jobs better every day?  How do we get them better access to information that otherwise they would be spending a lot of time just looking up?  The second layer is really how do we measure what we do?And so there’s a lot of thinking about, well, what are the right metrics to use and how do we think about that when we are trying to become a place that’s a home for creative freedom?  How do we build metrics and tools and use data so that we’re supporting that, not overriding it? 


Lyle:                It’s interesting.  I think building a place for creative freedom and giving a lot of knowledge about what’s happening kind of goes hand in hand. Like here’s a whole bunch of freedom and, by the way, these decisions have these kind of results.


Jen:                  Exactly.  Yeah.


Lyle:                Really empowers a person to make the right decision.


Jen:                  Absolutely, yeah.  I mean, I think the way that I always frame it with people is we’re trying to help people choose from among good decisions, not spend a ton of time needing to define those decisions to start with.


Lyle:                What problems are your team currently solving in production now?


Jen:                  There are a few big challenges when we think about applying data to production.  And, honestly, the first one is just visibility.  When you look at any one production, the way that that’s typically approached in industry and very much the case on a lot of our productions still, there are literally hundreds of paper forms that people will fill out as part of a production.  And those are all owned by different members of a production team.  And it can get very messy and very siloed. 


Lyle:                What are these papers that they fill out?


Jen:                  Cast lists, purchase orders, vendor lists, contracts, union documents.  Really, the list goes—I don’t even know all of them off the top of my head.  The list goes on and on. 


Lyle:                But that’s, I mean, that’s the traditional model.  That’s years ago.  I mean, I think—


Michael:          Sounds like it still happens today, Lyle.


Lyle:                Yeah, that does sound like that.


Jen:                  It does still happen today.  And to a certain extent, you know, we’re not trying to replace all of that.  Right?But there are opportunities for us to think about, okay, how can we actually use that information and extract some value from it?


Lyle:                So your team is at some level looking at getting rid of that paper or streamline that paper?  You know, where do you fit in that right now?


Jen:                  So we aren’t necessarily the ones looking to digitize that.We’ll partner with the engineering teams that are.  But what we’re looking to do is take that information, get some insight from it. 


Lyle:                Is that group, the engineers that is actually digitizing that the studio in the cloud?


Jen:                  Studio engineering, yes.


Lyle:                Studio—Okay.


Jen:                  So what I, what was interesting to me when I started at Netflix was the types of questions that I thought this team would answer are very different than the types of questions we actually answer or the way that we approach them.


Lyle:                What’d you think it was going to be?


Jen:                  I thought it was going to be things like, you know, is this production running late or things that were kind of very micro type looks at things. And in reality, nobody will know a single production better than the people actually working on it.Like there’s nothing I can tell you about Glow season two that the people working on that don’t already know better than me.  But what—


Lyle:                Right.  Because they’re there every day.  They’re managing the people there.


Jen:                  Exactly.  But what may not be immediately obvious is if we look at half-hour comedies, right, or if we look at things that film primarily in Los Angeles or if we look at—I don’t know—shows that use a certain kind of equipment, right, is there some insight we can get from that so that we are giving our creative teams more space to think about being creative as opposed to worrying about the logistics or the challenges that come up with all of the details.


Lyle:                I’m having trouble seeing how that would help the actual season two in production kind of thing.  But I would imagine prior to starting it might be extremely useful.  You know, if we move this to outside of Burbank, we go up in Sunland area or something and film at this warehouse, it’s going to save us this kind of money.  I can imagine that from a planning perspective.  But once a production’s in flow, is there something you can, is there also something you can help with?


Jen:                  There also can be, yeah.  One of the other things that we think about is—And I mentioned one challenge is just visibility into things.  Right?  And that can help prepping for new things that we haven’t started yet.  But another place my team really helps is in optimization.So if we look at the actual production schedule, right, that’s another example of where can we actually use data to make the process of defining a production schedule less arduous?


Lyle:                And less wasteful, I would assume as well, potentially.


Jen:                  In terms of time, money…


Lyle:                Yeah.  Just make more efficient.


Jen:                  Yeah, exactly.


Lyle:                It’s a hard problem of course because you’ve got a thousand people working on a show and they have their own needs and desires and issues.  And so you’ve got to line that all really well.  But so what are you focusing on with regard to schedule?


Jen:                  So one option there is the process of defining a schedule for a production is a very lengthy process.  And for good reason.  Right?  Usually an assistant director will sit down.  They will read through a whole script.  They’ll think about what the right schedule is.  And then they’ll construct that manually.  And there’s a lot of domain knowledge that goes into that.  But—


Lyle:                And are they—Just to interrupt.  In that situation they’re talking, you’re talking about scheduling all the crew—the actors, the direction staff, the PAs, lighting, all that stuff.


Jen:                  Right.  We’re going to film this scene on this day at this time in this location.


Lyle:                Okay.  And location.Okay.  Go on.


Jen:                  But, you know, so there’s a huge amount of context that we can never use data to replace.  But what we can do is, again, back to that theme of let’s give you good options to discuss versus needing to spend all your time manually defining them to start.  Can we actually think about generating some draft schedules?  So that if something unexpected happens on a production, someone breaks their leg and the whole schedule changes, you’re not back to the drawing board needing to manually redo this.  So we’re trying to think about ways where we have those opportunities for things that are also live in production as well.


Lyle:                I like the idea that you’re focusing on not replacing the expertise but empowering the person with better toolsets.  And even in like full schedule, calculation automatically, and then allow the person to go this piece of this is good, this piece was not.Besides chatting with us and wasting your time, what are you doing up in LG right now?


Jen:                  Well, I’m here for this.  I’m also here for—


Michael:          She rolled her eyes, by the way.


Lyle:                I know.  I saw. 


Jen:                  I’m also here for, we have a science and analytics all-hands that happens.  I think we do that quarterly.  So I’m here for that.  And then—


Lyle:                What kind of stuff do you talk about in that?


Jen:                  A lot of different things.  You know, there are so many different parts of the business where we have S&A teams embedded that it’s a chance for us to all kind of regroup and learn what everyone’s been working on.  It’s a chance for us to ask questions of our leadership about, you know, QBR, the quarterly business review, things that are on our minds, things that we want more detail on.  Also a chance for us to do some team breakout activities.  This time we’re focusing on inclusion specifically and talking more about what does that mean at Netflix, how can we all get better at that.  So it, the agenda will vary.  But it’s a chance for us all to get together as one big group and kind of see where we are and where we can get better.


Lyle:                The science and algorithms people that I work with have to do with looking at AB tests that we launch on the service and kind of analyzing them to see if, you know, did this thing we try work or not?  When your, right now when your engineers are, and when you had that role yourself, what data were you actually processing?


Jen:                  So we are working primarily with production related data.So it’s very—


Lyle:                Yeah, like you talked about all this paper.  But like what is it, how does it, how does that translate into a large dataset to actually analyze?


Jen:                  So if you had to think about some of the data that we might, what would you, what might you posit?


Lyle:                I can imagine you’ve got spaces that we’ve rented, you’ve got people’s schedules and expenditures.  And I’m assuming we have a lot of contracts with different houses.  Right?  So we’re going to have a contract with a large costume house or we’re going to have individual costume artists that are traveling out and renting and things.  So all the invoices for all those things we’ve got, with some data about them.  I just can’t—When I think about the data we have on, you know, whether a person liked watching something or not, it’s so much more clear and there’s so much more of it. It feels like you’d be scraping to get enough data.


Jen:                  Yeah.  I mean, the scale of the data’s definitely different.  So the challenge is that another team who’s dealing with like streaming data… Right?  That’s massive data volume.  The types of challenges you deal with with that type of data are very different than what we think about.  For us, things like quality and coverage and consistency are really nontrivial because most of our data comes from people.  It doesn’t come from an automated pipeline or process.  We’re not thinking about optimizing data pipelines.We’re thinking about data consistency is the number one challenge.


Lyle:                So part of your role as the studio grows quickly is to figure out ways to make sure that we’re learning from what we’re doing.


Jen:                  Hm-hmm.


Lyle:                And so that includes probably your team is like how do we get this HR system, you know, timed cards inside our system so we know how many people are working on the set.  Things like that.


Jen:                  We work very, very closely with the engineering teams that are actually building those systems to capture and start to digitize that data so that our team and then the data engineering team are sort of voices in that decision making so that the data model that those tools ultimately feed into is something we can use and build off of.


Lyle:                You mean data model as in machine learning model.


Jen:                  Also the way that the data is just stored in our data warehouse.  Making sure that the, kind of the linkages between all of the information’s consistent.  When we’re thinking about, you know, using an ID to represent something, that that’s consistently represented across all of our sources.  There’s also building out machine learning models from it.  But just starting from that place of like when I say Person ID 1 is Brad Pitt, Bradley Pitt, Brad Pitt III and, you know, B. Pitt.


Lyle:                Those are all the same person.


Jen:                  I’m all talking about the same person.


Lyle:                Right, right.  Well, okay.  Let’s talk about like last quarter, your team.  What was the biggest impact for the business that you guys did?


Jen:                  Well, last quarter my team was officially created.  So we grew.  So this quarter—


Lyle:                So this quarter, what is your plan?


Jen:                  So this quarter we’re working on a couple of exciting projects.  One of them is looking at how can we—You know, now that we’ve actually hit quite a good point in terms of the coverage and the availability of a lot of our really critical data.  So one big project for us this quarter is how do we start presenting that to these teams in a way that is useful to them?


Lyle:                You’re just not going to give them spreadsheets with numbers, huh?


Jen:                  No.  We’re trying to make it—


Michael:          Spreadsheet with a graph helps. 


Jen:                  Yeah, and add a map in and then you’re really cooking with gas.  But we’re—


Lyle:                So at some level you’ve got to find out from these people what kind of information would be useful to them.


Jen:                  Exactly.  And how do we represent that information in a way that will be consistently useful?  And what I mean by that is the types of challenges or questions that someone working on unscripted content has will often be quite different from the types of questions or the approach to those questions coming out of the feature films team.  So we have all this data.  Now how do we actually make it useful and insightful?


Lyle:                What’s one of your, one of the people you’re targeting to help them?  What kind of role we talking about?


Jen:                  On…  It will vary, I think.  Production coordinators are a great example.


Lyle:                Okay.  So define the production coordinator.


Jen:                  They’re the people who coordinate the production.  They—


Lyle:                Do they schedule everybody’s time?  Are they ones that—


Jen:                  Not directly.  So when I say production coordinator, I’m referring to people who are internal to Netflix.  So when we as Netflix are trying to keep track of, you know, what’s the timeline on something, where are we filming, who’s involved, how much are we spending…


Lyle:                For that one show, we talk to that one person because they’re like the point of reference.


Jen:                  For that one show, we talk to that one person because they’re the point.  Yes, exactly.


Lyle:                Okay.  So the show runner, whatever’s going to talk to that person and kind of feed them information all the time.  That person probably won’t be on set, but they’ll be aware of everything that’s happening.


Jen:                  Hm-hmm.  And they also would be the person that when we look at—Like a post coordinator, for example, would be the point of contact for, okay, we’re working with this vendor and they’re on this schedule and they’ve delivered this and we’re behind on this.


Lyle:                In post, you mean like after footage is all done, we got to edit it, we got to close caption it, we got to do the color correction, all that stuff.  There’s someone that’s managed that whole process as well.


Jen:                  Throughout, actually.  So although, you know, when we sort of draw out a timeline of what it takes to produce content, post shows up near the end, in actuality—


Lyle:                You would think, yeah.


Jen:                  Right?  In actuality, they’re actually quite involved throughout the whole thing.  Because they are, yes, they are actively working on all of the footage and the assets that we get from a production.  But we have to define what those assets are before we start production so we know what we’re making.  So they’re actually quite involved even from the very beginning. 


Lyle:                And this is how you get, you might add a pickup shot that, or you might have a second day where you do some pickup shots.  That’s happening because post was kind of involved in like we need more transition here or some kind of definition that happens by looking at the content.


Jen:                  It could be.  Yeah. 


Lyle:                So when you’re thinking about these production, the post-production manager…  Is it manager?


Jen:                  Coordinator.


Lyle:                Coordinator. 


Michael:          Production coordinator.


Lyle:                Yeah.  When you talk about those coordinators, have you been able to find out from them what kind of things they don’t have, what kind of stuff that would help them?


Jen:                  Yeah.  You know, often it’s not that the information doesn’t exist.  It’s just that it’s cumbersome to collect or it’s represented differently or it’s in six different spreadsheets for six different shows.  So it’s a lot of the questions that, you know, are really just help people do their jobs every day.  Right?  Make that easier.  Where are we filming the majority of our shows?  Who are we working with?  And who do they tend to work with?  What is the total cost for, you know, X, Y, and Z?  So it’s very, very like kind of direct questions.  But making that much easier for them to access is really important.


Lyle:                It seems like the actual interface to this data is going to be highly attuned for every one of these roles.  I mean, in the sense like how do you do it at scale?


Jen:                  Yeah, I mean, that’s what we’re trying to do.  Right?  It’s, there’s sort of this balancing act that we play between how do we make something aligned with individual workflows but also broadly interesting or broadly insightful?And that’s something that we figure out with feedback.  You know, nothing that we make is like one and done.  You know?


Lyle:                Set in stone, yeah. 


Michael:          Is a lot what you do reactive into like requests coming in for what people need?  Or is it more proactive, like what are the people going to need?


Jen:                  It’s a little bit of both.  You know, I would say that if you think about our approach to content at Netflix, we have a very robust set of tools and metrics.  And even our content planning and analysis teams are very kind of data literate.  They’re really well aware of what data mans at Netflix and how to best leverage it.


Lyle:                And almost across the entire company.  Up here at least, the engineers, you know, we all think about data as one of our core pieces of knowledge about—I mean, we don’t start a project without understanding how we’re going to measure it.


Jen:                  Yes.  And I think that for the studio, which is one of the newer if not newest parts of the Netflix business, we’re sort of beginning on that journey.  We’re on that journey already, but we’re closer to the beginning of it.  So we sort of have this balance of, well, someone has asked to have insight into something.  Okay, we’re going to help them with that.  But often our work comes about through discussions with our stakeholders.  Hey, tell me what’s top of mind, tell me what’s challenging.  You know, when you’re trying to answer a quest-.  Like what are questions you’re trying to answer over and over again? 


Lyle:                Yeah.  It’s almost like a coach. 


Jen:                  Yeah.  Sort of coach or sort of like a data advisor kind of role.  And then a lot of our projects grow organically from that.


Michael:          So’s from my limited time in LA, as Lyle will attest to, that I see that at least what has been told to me is that there is some hesitancy of going kind of more into this technological world from the LA perspective.Because there’s a very traditional media kind of establishment.  Everyone has a process.  Everyone’s used to a very—


Lyle:                People have worked on shows and they’re used to it that way.


Michael:          It’s the same thing on every show, so no one gets it wrong.Right?  It produces more consistent results.  How much—Kind of a two-part question. A, is it hard to start moving, you know, the other side of these productions to that?  And, B, have you ever had those moments with teams where it’s like the wow factor, like how could I ever go back to the traditional one now that I’ve had enablement by data?


Jen:                  Yeah.  You know, I would say on the wow factor piece, you know, my experience is primarily with Netflix internal folks.  Right?Like my direct stakeholders and my team’s direct stakeholders are not the line producers.  They are the, you know, VP of original series.  And I’ve been consistently impressed actually with, even for folks who are coming into Netflix with decades of experience in the entertainment industry, that like kind of foundational aspect of our culture around leaning into data and thinking about how we get better at things is something that they fully embrace.


Lyle:                And that’s good, because they got hired.


Jen:                  Yeah, exactly.  Right?  I think that’s a huge part of why Netflix is such a great home for them is because they’re super enthusiastic about this stuff.  I’ve talked to some of the folks in LA about, you know, oh, well, like what if we just put this data on a map?  You know?And we just put it up there and make it really easy to filter and search and whatever.  He’s like, “Yeah, you know, when I was a production assistant years ago I used to have to like search for all of this and do it manually and it took forever.”


Lyle:                Use a Thomas guide.  Yeah.


Jen:                  And like this is like, you know, in like beta Google Maps.  And like so it’s really exciting to talk with people and say like, okay, that’s really cool.Like the solutions we’re thinking of here are things that they’ve actually been thinking of for a long time and now we can make it possible.  So there are a lot of those little wow moments.  I think on the industry more broadly, I think thus far we’ve been very fortunate to have piloted a lot of the production facing tools that our studio technology team builds.  We’ve been able to pilot them on Netflix productions that are really excited about this new technology.


Lyle:                Let’s talk a bit about your team and you transition actually.  So you were an engineer working with data yourself.  You’re now a manager, relatively new.  How did that transition happen?  How did your career growth occur that way?  Did you say I want to do that?  Or, you know, what occurred?


Jen:                  So I’d joined Netflix as an analytics engineer.I was actually managing a team prior to joining Netflix.  Came in here because I was really excited about the team and the work.  And, you know, it just happened to be a part of the business that was growing very quickly.  And I didn’t even realize that. You know, I don’t think I fully realized that when I joined.  And, you know, the production space in particular needed a lot more support.  So there was a manager role open.  And I said to my boss like, hey, I’m interested in this.  You know, let’s talk about it.  And, you know, one thing sort of led to another.  And he was like, “You know, I think you’re the best person for the role.  You know, you’re—”


Lyle:                And it helped that you already managed people.


Jen:                  It helped that I had already managed people.  I think it also helped that in the time that I was at Netflix in an individual contributor role, I was there in LA already.I already had good relationships with those stakeholders.  I had already been thinking about, you know, where do we really need to go with this space, with data.  And so it was sort of a natural progression. 


Lyle:                What kind of people are you looking to join your team?


Jen:                  People who are really excited to figure out ambiguous problems.  And I think that the kind of technical requirements aside…


Lyle:                Right.  You can read the job description, friend.  Yeah.


Jen:                  …it’s more that, you know, at Netflix we talk about how every individual contributor is still a leader and an owner.  And that is especially true for this space.  You know, I’m looking for people who, you know, I can look at them and say, hey, like go figure out what we do with production finance data.  And they’ll say, “All right, great.  Like I’ll go figure it out and then we can talk about it.”  So that kind of scrappy like let’s figure it out kind of attitude is very exciting.


Lyle:                And be comfortable talking to a lot of people.


Jen:                  Yeah. 


Lyle:                Yeah.  Yeah, that’s good.  If you were doing something else, if you were in some other field, what would it be in?I mean, still data analytics.


Jen:                  Okay.


Lyle:                I’m saying not in entertainment.


Jen:                  I started my career in consulting.  And there, you kind of hop around to different industries depending on where your client engagement is.


Lyle:                So you’ve seen a lot of industries.


Jen:                  So I’ve seen a, I’ve seen quite a few.  I don’t know that I have a specific preference.  I mean, like personally I’m really passionate about travel and like outdoor experiences and adventure travel.  And so maybe looking at travel and hospitality.


Lyle:                Do you think the consultant role and the kind of, you know, coming in and working on larger projects for somebody else, does that fit the, is that a good description of what you do continually here?


Jen:                  Yeah.  You know, I think that that experience really prepared me well for this role and others in my career.  Where you basically step into a new problem space, you figure out what the problem is, you figure out how to prioritize it, and then you come up with a solution.  I think there are a few things about consulting that are very different than how we think about our work at Netflix.


Lyle:                Like what?


Jen:                  Big one is hierarchy.  So when you join a large consulting firm, you typically come in as an associate then—And you’re doing a lot of the day-to-day grunt work.  Then when you’re a senior associate, you’re hopefully doing a little bit less of the grunt work.  Then you’re a manager, then a senior manager.  Then you’re a partner.  And as you go up that ladder, you know, you’re becoming more and more of a, sort of a salesperson really.  You’re trying to engage with clients to get them to use your consulting firm services.  And that hierarchy is very entrenched.  Right?It’s the up and out model.  And while that works extremely well for that type of work, it’s not always as conducive to risk taking and creative solutions, which are two things that we really value here.  So I’ve been glad to have that sort of mind shift and experience the difference here.


Lyle:                How flat is it here?


Jen:                  Pretty flat.  And I think that will vary a bit based on the teams that you’re on.  For our org in science and analytics, we do have different titles.  But it’s not a strict, you know, up and out model.  You know, we’ll have directors who report to directors.  You know, really moving into a manager role here is more about becoming a people manager.  And other companies, including the one I was at previously, it’s very much still the player coach model where you’re expected to do quite a bit of hands-on development still.  And I can guide and I’m here to be a sounding board for people on a technical approach.But really my job is to build the team, to build relationships with our stakeholders, to be more of the coach, you know.


Lyle:                To figure out how much investment in certain areas.


Jen:                  So it’s a bit different. 


Lyle:                Are you missing the code aspect or the analytics aspect?


Jen:                  Not too much.  You know, I mean, I still see it because my team members are working on it.But I’m much more excited about the kinds of questions that I get to think about now, which are things like how do I help someone, you know, think about where they feel strongest?  And how do I find those opportunities for them?  And how do I help them grow?  And if I think about where this space needs to be in five years, what are the steps we need to take to get there?  Not necessarily how do I write the code that gets us there.


Lyle:                How do you even figure out what a space can look like in five years?  I mean, we launched something like 750 titles this year is kind of our, the numbers I’ve heard.  That’s a lot of productions.  And since you’ve got, you know, a movie’s going to be very different than an unscripted content thing.  How do you even figure out what parts you need to answer? 


Jen:                  Yeah.  You know, sometimes it feels like a shot in the dark.  But that’s when I usually will go back to, you know, like the people leading those teams.  And they, you know, they were hired for a reason.  It’s because they’re experts at that kind of work.  And so I really lean on them to say, hey, you know, where do you want your team to go?  And then I can think about, okay, how do I use data to help them get there?  But a lot of those answers, I don’t have them when I start out.


Lyle:                That’s an exciting place to be.


Jen:                  Because I didn’t come from the entertainment industry.  So I really lean on the people who do.


Lyle:                What’s the best thing about working here?


Jen:                  I have a lot of ownership in what I do.  I mean, I felt like that when I first started as an individual contributor, throughout that time, now as a manager.  You know, if I want to take a risk, I can do that.  If I think there’s a better way to do something, I can say that.  And that can be to my team, that can be to my boss, to my boss’ boss, all the way up.  Right?  I feel like I’m really trusted to make those decisions, and that helps me grow a lot.So I feel like there’s a lot that I can learn just by being here and being around really smart people.


Lyle:                So a lot of times we kind of refer to that I think as freedom in responsibility slash judgment.  But in the freedom in responsibility area, sounds like a lot of what you do is reacting to or trying to think about what is good for, you know, these production coordinators.  How often is it that an IC’s like, oh, I have this really great idea.  I want to kind of just drive this.  How does that work?  And how often does that happen?


Jen:                  Happens all the time.  I have quite a few newer folks on my team right now.  So I’ve been a little bit more involved in like, hey, here’s a great area, go check this out.  But now that they’ve kind of been here for a little while, they’ve got their footing, I hear suggestions all the time.


Lyle:                Thank you so much for doing this with us.  I really appreciate hearing a little bit more about it.I did not know that your team even existed until you sat down in the room.  Well, earlier today when I was reading up about what you do.  So I think it’s fascinating that we keep on throwing analytics and science at our problem spaces.  So thank you for leading that and working on that.  I want to ask you what are you currently watching?


Jen:                  I am a huge fan of Last Chance U. So—


Michael:          Ooh, so am I.  Have you seen season three?


Jen:                  Yes.


Lyle:                Describe what Last Chance U is.


Jen:                  Oh my gosh.  Last Chance U is, it’s a documentary series—we have three seasons now—about…  It features college football programs for players who have been for any number of reasons kicked out of larger programs.  So it’s sort of their Last Chance University.  And it chronicles, you know, how they work their way back into those programs or find success by their own definition.  And it’s really eye opening.  Like it’s incredibly humbling to see where some of these kids come from and how hard they’ve worked and how much football means to them.  My husband is a huge football fan.  He played for quite a while.  And so when I first—


Michael:          So you guys, when you’re watching it together, it’s like…


Jen:                  Yeah.  So when we’re watch-, he’s like, “Oh, like I know the guy he’s talking about” or like, “Oh, like they should’ve done this play.”  I’m like, okay, great.  You know, I’m like bare-, I can barely name the NFL teams.  But it’s just, it’s amazing to see how much football means to them.  And it’s sort of opened my eyes to like, wow, this is really powerful.  Like this is actually life changing for these kids.  And I think it’s incredible.


Lyle:                Well, thank you again, Jen, so much for being here with We Are Netflix.  You have any other final things we didn’t ask you about that you might want to…


Jen:                  I don’t think so.  This was great.  I’ve never done a podcast before.  So.


Lyle:                Well, you sound like a pro.


Michael:          Yeah.


Jen:                  I have my yoga teacher voice on.


Michael:          Is that why I’m so soothed right now?


Jen:                  Yeah, exactly.  It’s…


Michael:          Namaste.


Jen:                  Namaste. Thanks, guys.


Michael:          Namaste.


Lyle:                This has been the We Are Netflix podcast.  I’m Lyle Troxell and my cohost is Michael Paulsen. Thanks for listening.