Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Angela Bassa, the Director of Data Science at iRobot.
Here is the podcast link.
Hugo: Hi there Angela, and welcome to DataFramed.
Angela: Thanks, thanks for having me.
Hugo: It’s a great pleasure to have you on the show, and I’m really excited to be talking about managing data science teams today, and your work at iRobot. But, before we get into this conversation, I’d love to find out a bit about you. I thought maybe you could start by telling us what you’re known for in the data community.
Angela: Sure. I’ve been involved in data science under that moniker for, let’s call it four, five years. Most of the contributions that I’ve made, you know I don’t have a package named after me, or anything like that. But, I’ve spoken a lot about how to do data science within a business context in terms of corporate data science, and also how to develop the skills to succeed as a data scientist within a larger organization. I’d like to think that folks who care about what I have to say, probably care about that.
Hugo: Absolutely. I think that’s really important, particularly at this stage in the way data science is developing to consider what’s happening in a business context, because a lot of people I think talk about the kind of state of the art techniques, and all of the buzz terms we hear. But, we do need to remember that data science within an organization is there in order to be one set of inputs into the decision making process, right?
Angela: Yeah, exactly. There are lots of companies that have their product be data scientific or algorithmic, but a large portion of data science performed within a business context is actually done in service of the business, rather than packaged as its own product. Really understanding how that fits within the larger organization strategically really matters in terms of being successful.
How did you get into data science?
Hugo: Yeah, and that’s a point we’re going to get to in this conversation. Before we get there though, I’d like to know a bit about your trajectory in terms of how you got into data science initially.
Angela: The short answer is I was really lucky. I can’t claim to have gone to school with the ultimate goal of being a data science professional, even though I’m really, really glad that that’s how things turned out. I went to undergrad, and I went to an engineering school, so obviously what you do in an engineering school is you don’t do engineering. I did my undergrad in math, and they recruit really heavily on Wall Street for math professionals. It was either that or academia, and I didn’t really want to join the academic environment, so off to Wall Street I went. I hated it. It was such a bad personality fit. But I did get to work with data. That was my role, was to do data analysis, and monitor data activity in the market. That part I really liked. Nothing against the financial world, I still have friends there. It just wasn’t for me.
Hugo: When was that?
Angela: Oh my gosh, that was about 15 years ago.
Hugo: Okay.
Angela: After I left, I started doing strategy consulting because that’s the other thing that you do if you don’t do investment banking. You usually do strategy consulting. That’s when I really started getting my hands dirty with data, and modeling in particular, rather than just monitoring. I did a lot of pharmaceutical strategies. There’s a lot of statistics that go into how do you set up a controlled experiment, a randomized control trial so that you can test the efficacy of different treatments. We did a lot of that kind of consulting for large pharmaceutical companies, and biotechs, and med techs. I did that for about eight years.
Angela: I left that industry and joined a large marketing services organization. That’s where I got introduced to big data. Truly big data. I mean, that’s when we went from things that could run on a single machine. I mean, the machine might hang a little, but it would definitely all run within RAM, to computations that you really needed to understand how to run the computation as much as you needed to understand what the computation was. While that was exciting, the stakes were really low. I mean, if you mess up, somebody doesn’t get a coupon, right? I actually, here in the Boston area where I’m located, participating in the community, and meetups, and what not, I ended up meeting some folks from a company that used to exist called EnerNOC. They’ve since been acquired.
Angela: That’s where the great outcome really came, where I was doing data science under that name. The stakes were high enough, so if you messed up you could lead to a brownout, or a blackout, or something like that, and folks really depended on our analysis to be able to save money, to save electricity. I really enjoyed working in that, but then after a while the great folks at iRobot reached out to me, and as a nerd, the opportunity to do this thing that I love, data science, but, also work with robots, it was-
Hugo: That’s pretty cool.
Angela: … Yeah, it was hard to pass. It was really hard to pass.
What do you do at iRobot?
Hugo: What do you do at iRobot now?
Angela: I am the Director of Data Science at iRobot. I think there are sort of two sides to what I do. One side is managing the team, a team of Data Scientists, and Analysts, and Interns, and Contractors who help us achieve our objectives. The other part of that is setting those objectives, and understanding how we can do the most good for the company.
Hugo: Great. Can you just tell us a bit about what iRobot does?
Angela: Sure. iRobot is the number one consumer robotics company. In the US for sure, I’m pretty sure it’s the world as well. We are the makers of consumer robots that you may know and love, like the Roomba, and the Braava. Those are a robotic vacuum cleaner, and a robotic mop, respectively. I think it’s really fantastic, because robotics is hard. This is a company that has figured out how to operate in this really difficult environment, making robots that are affordable, and that are able to help folks who have these tools sort of do more with their day. It’s really exciting.
Hugo: Now I kind of want to delve into a bit about data science management. As we know, there are a lot of roads that lead to data science, and I’m sure there are a lot of roads that lead into becoming a data science manager as well. Maybe you could tell us how you actually got into this position, or into data science management in general?
Angela: Yeah. I think a lot of folks who get into management for technical fields, have a similar background. Which is that, you usually excel as an individual contributor, and then get sort of promoted into this completely different discipline. It’s funny, because a lot of the heuristics that make you a really good individual contributor, don’t necessarily translate when you go into management. As an individual contributor, you are answering questions, and posing questions. Really as a manager, you’re scaling humans. It is a completely different discipline in and of itself. It takes time and effort to get really good at it, and I think the first step is understanding that it’s a different job.
Hugo: And a job that you’re not necessarily, will have ever been trained to do in terms of being a successful individual contributor in your field of expertise, right?
Angela: Yeah, exactly. As an individual contributor you may get your toes wet, in terms of providing mentorship, or working closely with interns, and helping them derive the most benefit from those kinds of relationships, those internships. But, going from being an individual contributor to being a manager, you have to remember that your goal is not to answer the question, your goal is to empower folks to answer their questions.
Hugo: What has your journey been like?
Angela: The way that I ended up in management, I think … Well, it’s funny. When I first was offered the opportunity to manage another person on a team, we interviewed several candidates, and the person who ultimately got the job. There was I think some miscommunication, because I don’t have a PhD, I just have my undergraduate math degree, which I think served really well for how I started, which was in, as a Data Analyst. Back in the dark ages, before Hadoop existed.
Angela: The person that we hired had a PhD. He had just graduated. He starts day one, he finds out that he’s working for me directly, as opposed to with me. He ended up quitting that day.
Hugo: Oh wow. That was your first engagement managing someone?
Angela: Yes, exactly.
Hugo: Wow.
Angela: That left a mark, so I didn’t end up-
Hugo: Mm-hmm (affirmative).
Angela: … I ended up, every time that I was offered a similar opportunity, I found a way to not take that on.
Hugo: Sure.
Angela: After-
Hugo: First impressions last.
Angela: … I know. Then after a few years, it sort of became evident that, that was what needed to happen for this situation, the context that I was in. I was really cautious and worried, and I also didn’t want to give up the role of individual contributor. Something that I had a lot of passion for, that I … If I may so, I thought I was really good at, and I enjoyed. I was worried that the path for professional growth lay in switching tracks, and jumping into management. That isn’t always the case, but it certainly seemed like the door that was open to me at the time.
Angela: I was cautious, and a little bit … It felt bittersweet. But, after that, managing a single person, and having that second iteration worked much better. I think probably because I was more self aware, and the person that I was managing had a much better temperament. After that I ended up managing a whole engagement, where the two of us worked. Then from there, it just kept growing, where I managed a small team, and then ended up managing the function, the discipline of data science within an organization. It has been an evolution.
Data Science Team Models
Hugo: Yeah, interesting. In terms of a data science team needing to deliver value for a business, we need to consider how data science is embedded in an organization. I’m wondering to your mind, what different models exist for data science in orgs, and which is your favorite, or which do you have, or which is working for you at the moment?
Angela: I have personally worked in data science under several different umbrellas. For instance, I’ve had teams that were under the operations branch of the org chart, under finance, and financial operations, under IT, under engineering, or under a dedicated R&D organization. Obviously, that’s a lot of organizational structure, so there were a couple of re-orgs. I’ve even been part of several re-organizations, and one thing that I noticed is that, the data science team always changes hands, always changes branches of the org chart, every time there’s been a re-org, that I’ve been a party of. I think that speaks to the value that data science can bring to an organization. That, it seems like different arms of a company all want to be able to leverage this really powerful discipline.
Angela: I think the key to ensure that the function is successful within an organization, independent of where it sits, whether it sits next to product management, usually when you’re developing product features, or whether it sits within operations, so that you are delivering value back to the enterprise. I think the important thing there is to really allow the function to mature. Usually in companies, especially companies where data science is not the product. Because, otherwise in those cases, data science is part of the founding, right? You need that in order to deliver on the business proposition.
Angela: But, in other cases within a larger enterprise, within sort of legacy companies that are looking to employ the tool set, there’s usually a couple of people who are delivering on that, and they really need time to mature as a discipline within the organization to become experts in the strategy, and the objective of that organization to be able to become experts in the data, and the artifacts, and bring that back. That’s the one thing that I would say that, everything else really doesn’t matter in an organizational context. But, what matters is the ability to let that team go through a couple of iterations, so they get to a point where they’re past the exploration.
Important Management Strategies
Hugo: I think this idea of allowing the team to become mature and evolve is incredibly important, and something we’ll come back to. Something you mentioned just then is allowing the team also time to understand the data, and become experts. I think this is in the direction of facilitating, allowing the team to deliver as much value to the organization as possible. I’m wondering, just in general, what are the most important strategies as a manager to ensure that your team can deliver as much value as possible?
Angela: … I think the metaphor that I like to imagine is, and you’ll have to pardon me because I’m Brazilian, so I think in soccer, not football. But, in American football there is this concept of the lineman who creates space so that the quarterback can make his play. I think a lot of times we like to think of the manager as the quarterback, and I don’t think that’s right. I think the individual contributors, for their particular projects, for their tasks, are their own quarterbacks. The role of the manager is to really create the pocket, create space so that they can think, and create space so that they can see the whole field, and they can see the opportunities, and they can see the answer.
Angela: That’s sort of my mentality. What I coach my team to be able to do is to become experts in the data. I think if you get asked to perform an analysis, or to answer a question, a lot of the times what happens is the person doing the asking doesn’t necessarily have the imagination to envision what an answer might look like, or what might be able, right? They have this narrow view, because they are experts in something else. They’re incredibly smart, but they’re smart in a different thing than the thing we are smart in. When they ask a question, sometimes the question is too low level, or too high level.
Angela: Part of the role of a Data Scientist is to be that therapist, getting the question just so, so that you really get at what the person doing the asking wants. Sometimes they don’t even know what they want, or they don’t even know what’s possible to get answers to. So being the lineman to create that space so that the quarterbacks can do their thing, and do the strategy, and figure out how to answer the question is really how I think of enabling the team to deliver the most value.
Hugo: There’s so much in there. Two takeaways, I was thinking of when listening was, there’s the aspect of managing expectations on both sides of what’s possible, what’s feasible. But, also this act of translation, and helping to turn business questions into data questions. Then, the reverse act of translation, turning the data answers into business answers as well.
Angela: I think that’s fundamentally the job of a Data Scientist. Because, everybody … I mean, it’s the 21st century. Every discipline has data, everybody has information that they’re using to inform their decision making. What makes data science unique is our ability to take a business question, and formally formulate it, formally articulate it in a way that we can use the tools of statistics, and in software development to create a solution that is reproducible, that is replicable, that is interpretable, and that is fit for purpose, that answers the question. Because, a lot of times, what can happen is Data Scientists will become so enamored with a particular approach, that they can try to use it for everything when it wouldn’t be a great fit. Or, they become enamored with a data set, and they use it because they can, not because they should. That translational step, from business, to math, to the technical components, back to the business, really is where the great Data Scientists make a difference.
Team Growth
Hugo: A recurring theme in this conversation so far is the maturing of a data science team, and the evolution as a team. As you said, you started off managing one person. I’m wondering, what are key aspects of a Data Science Manager to think about as your team grows in size as a function of time?
Angela: I think one of the things that happens over time, so I was the first Manager of Data Science at EnerNOC, the company that I was at before iRobot. I was the first Director of Data Science at iRobot, so these are two teams that I grew sort of from the ground up. The thing that happens in the very beginning, there is so much potential, but there’s also so much low hanging fruit. Having a team that has the flexibility to deliver on a couple of … I wouldn’t call them necessarily moon shots, but on a couple of high visibility, high sophistication answers, to start illustrating what’s possible, right? What are the amazing things that this new function can deliver on?
Angela: But also, that low hanging fruit. The quickest way to value is knocking those out, is taking the things that are easy, and answering them better than anybody else can, with an architecture that takes care of itself so that it requires minimal monitoring. You just start adding things to that pipeline, and solving problems that themselves are tiny, but that save an aggregate number of people seconds or minutes. Then, those add up. Having that flexibility means that in the very beginning, you have sort of undifferentiated talent, right? You have the quote/unquote, “Unicorns.” I hate that word.
Hugo: So, data science generalists of some sort?
Angela: Exactly, yeah. Folks that have the basic tool set, and that with a little bit of guidance, can sort of play in all of those roles. But there’s something that folks in the medical sciences say, that I think is really relevant, is that ontogeny recapitulates phylogeny. And what I think that phrase means is that the way organisms develop, from fertilization to gestation or hatching, mimic the stages in the evolution of the animal’s remote ancestors. That’s a pretty random analogy, but the way I think it’s relevant here is that the way a data science team develops also mimics the stages in the evolution of a company. So, much like a startup, a budding data science team has lots of undifferentiated and flexible talent, and the team goes through several “pivots” and they try to establish their value, who their champions are, and the ideal way to engage with the rest of their internal customers. When they’re just small teams, they are rudimentary, and they are pluripotent, right? They act a little these stem cells, right? They can mature into anything.
Hugo: What then happens as a data team matures?Angela: When you get some maturity into the team, that’s when you start to have specialization, that’s when you start to have differentiation. That’s when you start having folks who are really great at visualization, or really natural talents in terms of data platform engineering, or reliability. Folks who are great at QA, they have that personality, and that passion for the attention to detail. When there’s enough scale in the type of work that the data science team is doing, only then I think it makes sense to start having those dedicated, ancillary teams that can liberate the Data Scientist to truly focus on the science component, which is the hypothesis testing.
Hiring and Building a Team
Hugo: Once you enter that stage, how do you think about hiring, or building a team around the different skills? As you say, you don’t necessarily want data science generalists then, but you want a team of people whose skill sets, questions asking, curiosities compliment one another, right?
Angela: Yeah. I think as you start, you want to have folks who are well rounded. But, the farther along you go, I think it’s important to have a team that represents your end users, whoever they may be. I think, especially in companies where the product is data science, you want to make sure that your data science team looks like the folks who use your product, so that you have different perspectives, and you can ask different questions. And, everybody doesn’t look the same way, use the same tools, ask the same questions. I think it’s important to have that diversity in all dimensions. I think folks who are senior, and folks who are junior, there’s something I like to think about in terms of the luxury of ignorance. Which, is when junior folks in a team get to ask “dumb” questions, right? And dumb in quotation marks, because they’re not dumb. What they are is unencumbered. They’re unencumbered by the assumptions that we forget we make. They’re unencumbered by the heuristics that we’ve developed, that may not be applicable everywhere.
Angela: They have this luxury of being able to challenge the assumptions that folks with more seniority are perfectly capable of, but you start forgetting. You hear hooves, and you think horses, not zebras. Well, the more younger folks are like, “Well what if it’s a zebra?” And they challenge that, and they force you to think about why you’re making certain decisions.
Hugo: I love that, and I love that you describe it in terms of heuristics that we develop over time, because we know that when we start using heuristics a lot, they’re coupled with certain biases as well. So, having a new point of view, which is unencumbered by heuristics will also make us recognize our own biases hopefully also.
Angela: Absolutely. Not to knock on heuristics, they’re great, they exist for a reason.
Hugo: Mm-hmm (affirmative) and necessary.
Angela: We build them because they create shortcuts, and they make us efficient, right? It’s the whole thing about thinking fast and slow, and how our brains operate, and how we create our own Bayesian priors, and sort of go from them. But, I think having folks with different priors participate in the conversation, really enriches it.
Hugo: You mentioned earlier the types of questions that a data science team can think about, and perhaps should think about. You actually … We may get to this later, recently you sent me a draft of an article you’re writing for Harvard Business Review, and you make a nice distinction there between the space of questions a team might be able to answer, and the space of questions a team can and should answer. I thought maybe you could speak to that a bit, in light of this.
Angela: Yeah. I think that this is a perfect fit for that. In terms of what usually can happen, and very easily. I’ve been guilty of this as well is, you have access to data, and so you start correlating. You start exploring, and you start figuring out what could happen. I think while there’s certainly value in directionless exploration as you’re starting to build your own heuristics about these data artifacts. I think if possible, whenever possible, it’s much more important to think about first what the objective might be, and to have that north star as you start quote/unquote, “Spelunking,” through data. When you think about what the question that is posed to you is, a lot of the times it can be easy to think, “Oh, well I don’t have a perfect fit for that question, but I have this other data set that I bet is correlated.” And so, you start going there.
Angela: I think one of the things that makes a really good Data Scientist is also humility. Humility to know that maybe that’s not what it means. I mean, sometimes the answer is in an email thread somewhere that you don’t have access to, that you weren’t involved in, that you weren’t privy to. But, the answer exists elsewhere. I think it’s really important to have the self awareness to go and ask, and become an expert not just through spelunking in data, but spelunking through the organization, right? Making connections with other folks around the organization, and truly gaining insight into how the data is generated, what context is it used for, can it be repurposed, what are the issues that potentially arise with that repurposing?
Angela: And so, really figuring out what kinds of questions can be answered is great, but what kind of questions should be answered I think is something that the data scientists within an organization is well positioned to be able to ask, and perhaps a better position than anybody else.
Trade Offs
Hugo: Now, this speaks to a certain trade off I think. I’m wondering, in your role as a Data Science Manager, what are the types of trade offs you need to make, and how do you think about making the right ones?
Angela: Oh, I think being a manager in any discipline, but especially in data science, I think those trade offs are everything. Data science is a little bit different than other types of work, because you’re not just answering questions. A lot of times you are figuring out if a question is even answerable, right? It’s not just the how or the what, but it’s also the if. Figuring out those trade offs, a lot of other disciplines have different trade offs. But, a lot of the trade offs are also very similar in terms of how much time do you spend catching up on what the latest findings of a discipline, the latest applications, the latest methodologies versus, selling a discipline, selling it internally, letting folks in legal, and in sales, and in operations. Letting them know that this resource is available to them if they have questions, that they would love to have more information, more data to help with their decision making.
Angela: How much do you spend doing? Usually I’m making slides, or writing memos, or thinking through what everybody needs, and articulating that, and setting it in writing. Versus coaching, versus growing your team, and making sure they have what they need, and making sure that they are getting exposed to strategy so that they can make the best play when it’s their turn. As well as planning, and strategizing, and figuring out who do we need to talk to, when do we need to deliver something by, when do we need to do road shows, and present some of our findings so folks know that we’re a credible part of the organization that can be leveraged, and can bring value?
Angela: I think all of that are the things that you are constantly trying to juggle and optimize as a manager. Also, there’s loads of additional questions. Who do you bring into your team, and how do you make sure that everybody who comes and joins the team allows you to get network effects out of that expansion, so that you’re not just having a plus one, but you’re having plus N because of all of the ways in which that person improves a team, and covers blind spots?
Hugo: How do you think about the trade off between … I mean, when hiring for a data science position you can hire someone with incredibly strong quantitative and data science skills. But, you can also go about it, I presume, in terms of someone who maybe has some other expertise, and can pick up a bunch of the data science in the process as well, right?
Angela: Yeah. I’m a big fan of the data science boot camps. Not all of them, but I think there are several that have been fantastic for preparing folks who have that ambition, and that ability to learn the skills, right? I think there are parts to data science which is, that you can’t teach, right? You can’t teach somebody to want to answer a question correctly. But, the how I think is teachable. I think there are a lot of folks out there who are breaking into data science. I mean, the different institutes and universities are only starting to have quote/unquote, “Data science programs.” I mean, pretty much everybody who came into data science over the last five years did something else as their training.
Angela: Here’s a perfect example. On the team at iRobot we have one of our Data Scientists who’s originally trained as a Marine Biologist. You would think, “What does a Marine Biologist do in a robotics consumer company?” Well, you’d be surprised, because it turns out that there’s a lot of research in her field. What she did, she did a lot of studies with pods of dolphin in the wild. She actually traveled all over, and I’m sort of jealous. It turns out that that kind of modeling expertise is really useful when you’re thinking about a fleet of robots, and how those robots behave-
Hugo: Oh wow.
Angela: … Independently, and dependently. You can think of a fleet of robots as a pod of dolphin in certain scenarios. Obviously it’s not a perfect analog, but a lot of the modeling becomes extremely handy. That knowledge exists in the world, and it’s just a question of how do you know to look for it there?
Hugo: Yes.
Angela: She brings that level of expertise to us. She’s an amazing Data Scientist. She has all of the qualifications to be a fantastic Data Scientist, technically. But, she also brings this added dimension that helps us solve problems differently, and I think better.
Hugo: Yeah, and of course being from academic research, or scientific research, knowing how to ask the right questions. But also, if she did a lot of travel, data collection, that type of stuff, thinking about the data generating process, how the data was generated, and how then you can model it is such a key part of what it is to do this type of work also.
Angela: Exactly, yeah. That’s one of the reasons that I’m a big fan of internship programs as well, because it can seem like grunt work, but it’s incredibly important grunt work, and I think we all did it. I mean, I did it when I was on Wall Street as well. As I was building my data sets, and I was building these databases that got monitored, I understood very intimately what I meant when I made design choices, and how my design choices propagated downstream so that what kinds of questions were easier to answer, and harder to answer, and why? What was my governance model, right? I didn’t have words for those things when I was starting out, but that’s what those are.
Angela: In our internship program we have folks become intimately familiar with data gathering, and data ingestion, and data management which, I think down the line helps them tremendously, because they are better able to understand context, and to understand how important it is to be respectful of those design decisions, and not use data sets for one thing, when they are really intended for another, and accounting for that.
Hugo: Yeah. Just quickly, for any of our listeners who are really enjoying this conversation and your work at iRobot, can they check out internship programs online or something along those lines?
Angela: Oh yeah, absolutely. If you go to our careers page and search for the data science internship, yes, if you’re interested, please apply.
Hugo: Fantastic. If you do apply, make sure to mention that you heard about it on the podcast.
Angela: Absolutely, yes.
Data Integration with Stakeholders
Hugo: Something you mentioned Angela, is this idea of it being a requirement in some ways to sell data science internally in an organization. Something I’m really interested in is how we’re going to see our data literacy spread across orgs, not only in data science teams. I’m wondering for you to have the best conversation with stakeholders, how much data do they need to be able to speak, or do you see a future in which C-suite and other stakeholders speak more data, and become more data literate?
Angela: Oh, I think the latter. I think it’s going to start becoming even harder to not be able to credibly discuss your product, or your strategy in a data literate way. I think the market has that expectation, I think it’s becoming table stakes. And, to be able to ensure that your strategic decisions are based on information that you have had the foresight to gather so that you can make the right decisions.
Common Pitfalls for Data Science Managers
Hugo: What are some common pitfalls or warnings you have for Data Science Managers?
Angela: One of my pet peeves is when a data team doesn’t know what data is available, and what it means, and how it can be used. I think the first thing that you need to do is have a big exploratory data analysis party, you know?
Hugo: Awesome. Data party, I love it.
Angela: … Yes. Dedicate some time, once a week, maybe 10% of everybody’s time dedicated to getting lost in the data, and truly understanding it, and setting up coffees with other folks in the organization so that you can ask questions about how that data is designed, and created, and collected, and stored, and labeled. I think that’s hugely important. I get really aggravated when folks think that’s a waste of time, because it’s undirected, and I think it’s hugely valuable if you’re going to be the expert on your company’s data, that you be the expert on your company’s data.
Angela: The other thing I think is to not over promise. One of the things that tends to happen is, folks know what’s possible, and so they paint a picture, but they forget to be pragmatic about how they’re going to execute. And so, not over promising is huge, but not under promising either. I think sandbagging backfires, I think you need to be able to accurately promise. Then, to deliver on it. That’s not just because it keeps you from that over/under promising situation, but also because it builds credibility. If you can accurately assess what your outcome is going to be, I think that lends credibility to the actual outcome as well.
Angela: I think one way to get to that point where you can promise and then deliver is to be honest, and to be transparent. Perhaps a little bit more transparent than with other disciplines, because the Data Scientist is trained to interrogate data, to interrogate situations. They’re going to be able to tell when you’re over promising, or when you’re under promising, or when you’re not sure of what the objective is. It’s really important to have that clarity, and to communicate that within the team, and within the organization.
Future of Data Science in Organizations
Hugo: Great. I think this has been a wonderful conversation about the state of data science management today, and your practice in particular. I’m wondering what the future of data science in organizations, particularly relative to the decision function, what this looks like to you.
Angela: It’s rosy. I think there’s job security in data science. It is definitely something that’s becoming more and more ingrained in the fabric of different organizations. I think that’s why it depends. The future is going to look different for companies that have their product be data scientific or algorithmic, versus companies that use data science in service of something else. I also think that the future looks different, whether the team is part of a public organization, a startup, or a large organization. And also, the time horizon. Is this a team that is exclusively research, and they’re working on moon shots? Versus, a team that is more operational, and is enterprise facing, and helping the company optimize its own functioning.
Angela: I think all of those have different curves that they’re on, but I think, in any respect, I can’t see a future where we’re not relying more and more on the expertise of folks who understand how to manipulate data.
Call to Action
Hugo: For all our listeners out there who are either Data Scientists, aspiring Data Scientists, or even have aspirations to get into data science management, do you have a call to action for them?
Angela: Well, I’m so glad you mentioned it, and actually you also mentioned it earlier during our conversation. I’m really excited, I just penned an article for HBR, and it’s actually part of a series that they’re putting together called, “Managing Data Science.” It’s an eight week newsletter that they’re putting together, that focuses on making analytics and AI work for everybody’s organizations. I have an article coming up, so by the time this podcast hits the wires, I think it’s going to be two or three weeks old. I encourage you and your listeners to check it out.
Hugo: Fantastic, and we’ll include a link in the show notes as well.
Angela: Awesome, thank you.
Hugo: Angela, it’s been such a pleasure having you on the show.
Angela: Oh, it has been my pleasure. Thank you for letting me nerd out.
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