Data Science Hangout | Rebecca Hadi, Lyn Health | Transparent & Visible Work

Hi, everybody. Welcome to the Data Science Hangout. If you're joining for the first time, I'm Rachel and it's great to meet you. Today actually marks a full year of the Data Science Hangout. And I just want to say thank you to everyone for making it what it is and for hanging out with us. From that first one, which was kind of an experiment of seeing how things were going to go to what it is now. This is always a favorite part of my week now.

We are going to take a break from hangouts in July as we prepare for the RStudio conference and some big announcements. So let us know in the chat too if you'll be there at the conference. We'd love to see you all in person as well. But we'll be right back with normal hangouts right after the conference on Thursday, August 4th as well.

If this is your first hangout, this is an open space for the whole data science community to connect and chat about data science leadership, questions you're facing, and what's going on in the world of data science. These sessions are recorded and shared to YouTube as well as the Data Science Hangout site. So you can always go back and rewatch or find helpful resources there too. We also have a LinkedIn group I want to make sure I mention if you ever want to continue certain discussions there.

But before we get started here, there's three ways that you can ask questions. You can jump in by raising your hand on Zoom. You can put questions into the Zoom chat and feel free to just put a little star next to it if you want me to read it. Or I can call you to introduce yourself and add some context too. But lastly, we also have a Slido link where you can ask questions anonymously as well.

I think you just shared it right when I said it. But we always want to create spaces where everybody can participate and we can hear from everyone, no matter your level of experience or area of work as well. But with all that, thank you again for joining us today. I'm so excited to be joined by my co-host today, Rebecca Hottie, Head of Data Science at Lynn Health. And Rebecca, I'd love to kick things off with having you introduce yourself and sharing a little bit about your work and introducing your cat here as well.

Rebecca's background and work at Lyn Health

Yeah, absolutely. Thanks so much for having both myself and my cat Jinx. She was really excited for the hangout today, clearly. So thanks so much for having us. Yeah, so my name is Rebecca Hottie and I'm the Head of Data Science at Lynn Health. Lynn Health is a early stage startup and we're focusing on serving members who have multiple chronic conditions and building a health care experience that really supports the whole person.

So from a data science perspective, my role and the work my team does kind of buckets into kind of three or four different areas. So at a high level, we're a very small team of a data engineer in my team, and we work together to build out our data warehouse. And that's where we ingest data from both our clients, our own electronic health record system and make it kind of easier for access and also trying to build out self-service capabilities for other other teams. So we aren't quite so much the bottleneck.

Another area is trying to support identification of members who might be a good fit for our program. And we do that by building algorithms to measure something called impactability. So in health care, you can imagine that, you know, you hear these horror stories of, you know, hospital stays that are thousands of dollars and very expensive medications and things like that. But not all of those things are like impactable, you know, like some of those things you couldn't really have done anything about. So we're trying to take the transition from just someone's risk, like how like, you know, kind of traditional health care space, it's like a risk score, you know, like what is your predicted spend? But we're kind of trying to shift the perspective to what's your impactable spend and using that as sort of a main signal for who do we think is a good candidate for our program who has the most spend that using our methods, we think that we can impact.

And then the third one is building internal risk adjustment models to help us normalize comparisons between different physicians. So we're focused on looking at outcomes. So one example is inpatient mortality. So we built a risk adjustment model that is able to sort of predict when an inpatient mortality will happen with a high degree of accuracy. And we can use that to adjust for providers case mix to say, you know, just because they see a sicker group of patients, we don't want to penalize them for that. But use that to be able to detect statistically significant variation in the in their inpatient mortality rates as an example. And we use that to inform who we want to refer to. So we want to send patients to doctors who have statistically better outcomes.

And then the last one is more of a kind of sales support focus. And we support our sales team. And one of the favorite things that we did was we built a R Shiny interactive sales tool for them. We got some, it was initially built as an Excel model, but we got some feedback that, you know, we'd love to kind of have a slider that we can do live in meetings. And R Shiny was a great tool to achieve that. So we have a like shiny server instance that we host that tool on.

Awesome. That's really helpful. And, and what is your data team look like today?

Yeah, it's a pretty small data team. So it's actually myself and one data engineer. So my role is, you know, it's a lot of hands on keys, individual contributor work, in addition to supporting, you know, the growth and career of my data engineer and making sure that, you know, he's able to learn different things and build relationships with the business. And then also that, that strategy component of, you know, what, what strategically will help the business? What does our roadmap look like?

Being the first data hire

Well, we, yeah, we've talked about that before on Data Science Hangouts, how, when you're the first data person, there can be, there's a lot of decisions that you have to make, and sometimes can be a little scary. But how do you handle being one of the first data people there?

Yeah, it was definitely a bit of a learning curve. I think that one of the biggest challenges I faced was initially just not having any of our own data. So for a while, when I first started, we didn't have any sort of products or systems that were generating data. So a big thing that I had to do was kind of search for what publicly available data could we leverage to answer some of the questions that we have. So that was definitely an initial challenge. And then also kind of more of an opportunity as a first data person was just helping the organization see the value of data science and really being an advocate for, you know, using the work that myself slash what grew into my team produces to really drive decision making.

So but, you know, to be honest, it can be a little bit isolating. And I've worked previously at larger organizations where I got Nordstrom, for instance, there was a large center of excellence. So I was a part of a community with, you know, I think it was around 100 people of different data science analysts, engineers, so it can be a little bit isolating. But that's why I love events like this, and other things in the community where I can still have those technical relationships, and you know, kind of talk to other people.

Absolutely. Thank you. Ishwar, I see you put a question to the chat. Do you want to jump in and ask that? Hey, hi, everyone. This is Ishwar. I currently work at Nissan supply chain as a data scientist. So yeah, I'm really excited to, you know, know about the how data science journey of Rebecca Harris has started in her organization.

And I was in the same situation when I started at Nissan, specifically in my department, my team. So I had, I had a lot of challenges would be a lot of unstructured data, like XML format and super unorganized, and then not having anyone to help for on the data engineering task and getting those data from, you know, multiple places into the organized structure, and then at least data, scraping from XML to structure tables, and don't even know where to store the data. So those were the initial phases. And I was, I didn't even have a data engineer.

To start, I was just, you know, dead end that I don't have anywhere, anywhere to go, kind of stuff. So, yeah, I totally, you know, feel your, how you might have started your data science journey. So just trying to understanding even more, like, how did you at least kickstart your journey, at least to prove the, you know, proof of concept to the organization that, hey, data science, if we grow from one data scientist and one data engineer to the larger data science whole team, then definitely the organization may benefit from that team. So what was the challenge that you faced to go and get started with your proof of concept?

Yeah, absolutely. So you're trying to understand, you know, how did I sort of convince the business about the value of data science, that it's sort of worth the investment? Of course. Yeah. Okay. Yeah. That's a great question. I would say that I'm still very much on that journey. You know, just given with the economy sort of being what it is, we've had to make some pauses in some of the hiring that I was hoping to do. So, but it's, I think it's less about, I feel like I've done the work to sort of convince the business that it's worth the investment. And now it's more just like, okay, when's the right time to sort of make that investment just based on some of the economic conditions that have shifted.

But I think in terms of like how I built that trust, I would say probably the first project that I did developing our pricing model was very well received by the business. And that was because it was sort of this vague question of, you know, how, like, how are we going to demonstrate like, how do we, like, where do we think that we're going to have the most impact? And also how do we do that without having any of our own data in-house? So it was a pretty significant literature review that I did trying to understand, you know, the AHRQ has a lot of really great research papers about the challenges of people who have multiple chronic conditions, they're over-indexing and inpatient admissions and ED visits as well.

And so how I was able to kind of convince the business of kind of the worth of my team was building something out of nothing, right? And so it was sort of like, if I can take these very distinct kind of publicly available, like research papers and build a cohesive story where it has to be based in reality and it has to be reasonable, but building that into an actual savings model that I, and I think a big part of it, you know, kind of jumping around in my answer, but I think communication is a really big part of it, honestly, and driving adoption of the work where, you know, I could have taken the approach where it's sort of like, you know, I'm going to go off and build this savings model and I'm going to come back and yeah, there you go. But I kind of took a different approach where, you know, I brought them along throughout the process where, you know, I met with them like every week or so, and kind of said, you know, this is where I'm at. This is what I'm seeing. This is what it's looking like. And then getting their feedback at that point, and then integrating that feedback as I went along.

And then the final delivery of the product where, you know, I walked them through it at a very detailed level and kind of answered questions. So I would say it's kind of about that, like bringing them along throughout the process and creating that visibility into the work kind of rather than, you know, I kind of sometimes have the urge to like put on my headphones and go in a cave for two weeks and like just kind of bang something out. But I have to challenge myself to really like bring my stakeholders along that helps them develop ownership. So I would say that that's, that worked really well for me.

bringing them along throughout the process and creating that visibility into the work kind of rather than, you know, I kind of sometimes have the urge to like put on my headphones and go in a cave for two weeks and like just kind of bang something out. But I have to challenge myself to really like bring my stakeholders along that helps them develop ownership.

Navigating HIPAA and tooling constraints

Yeah, I would say that I haven't really faced challenges from our IT department in that respect. It was a while before we onboarded any sort of like detailed, you know, like information that contained PHI or protected health information or personal information. So it was a while before. And kind of at that point, the IT organization was kind of pursuing on this like SOC 2 kind of audit track. And so I think maybe the reason why I didn't have that challenge was because I have a really great relationship like with our CTO, with our IT person and kind of just, again, kind of goes back to communication, like kind of telling them what I anticipate like the needs will be. And so they were fine with us kind of standing up a Redshift instance.

I would say the one challenge that we had is that we were initially planning to use Tableau online as a data visualization, like kind of hosting it. But we had some confusion on whether or not that was HIPAA compliant. We had done some research that, you know, Tableau online was HIPAA compliant. But then when we actually went to sort of go push data up there, we realized, well, no, actually, because it's like Tableau hosting it, like their backend wasn't HIPAA compliant. Whereas like, but we didn't have the IT resources to stand up our own Tableau like server instance. So we had to pivot to using Amazon QuickSight just for kind of like this dashboard that we were building. So that was definitely a pivot where we didn't have the budget to or the like resourcing to stand up like a Tableau server, even though for me, Tableau is a little bit easier to use the QuickSight.

Transparent and visible work

Great question. Rebecca, I love the point you made about communication too. And just like even when you want to go work for two weeks in your cave with your headphones on, making sure to have regular check-ins with the team. But I was curious what that actually looks like in practice. Do you just keep sharing with the team or just have regular check-ins scheduled?

Yeah, I would say that my approach has evolved over time. We're a pretty like SOC-based company and I'm a big proponent of transparent work and visible work. So a couple of things that I do, we have a cross, like a channel, a data science channel that's open to the company. So we have like our team channel that's more about like, I'm going to be late today or whatever. But we have an open channel where anytime that we, like let's say that we do an analysis for the sales team, we post it in that channel and tag the sales team. So like anyone in the company can see that work. And we kind of try to keep conversations in that channel versus kind of DMs. Like we'll route people back to there, like let's have that conversation. So because a lot of times someone else might be interested in that work.

So that's one way. And then the other thing that we've done is we've created just like a Google sheet that we call our insight repository. So, and we have that pinned to our channel. And so when we post those analyses, we also put them in our insight repository where it's just like one row per analysis. That's like the analysis wise, like inpatient mortality model. Here's a link to the PDF. And then here's like kind of the insight or like the headline and then a link to the GitHub, but that's just more for our team. So that insight repository has been really well received as a place that's like, okay, if you have a question, like start here and you might just be already to have that.

And then kind of the third, I guess like maybe there's two more things that I do. I host a weekly like data science prioritization meeting. We use Monday as our project management tool. I've used here in the past, but I actually like Monday. It was kind of new for me to use, but it's really snazzy. And there that's been really helpful from a prioritization perspective, because I've had some challenges in the past, struggling requests from like our clinical team versus our sales team, like what's the priority. And it can be kind of hard to be that middle person that's like, sales team won this time. So like, sorry, clinical team, I'm not going to be able to do that kind of, not like a shoot the messenger situation, but it's a lot more productive when we can all have that conversation live and say this, sales team might say, I need X, Y, Z for a meeting. And we might say like, okay, that's going to push off these additions to this clinical dashboard. And then the clinical team is like, okay, we're okay with that because of like X, Y, Z reason. So it's just helpful to have that like kind of prioritization meeting where there's the list of work and people can see it.

And then the last thing is just kind of having, I'm a really big advocate. We have a monthly like all employee call and I try to get airtime there like pretty frequently to just kind of talk about, you know, initially like a level set of like, this is what data science is and how my team operates. And a big part of that that's been successful for me is kind of setting this idea of like, if you ask me something, I'm going to ask you like the why behind it. And I've had instances in my career where people can get kind of defensive when I ask that question, you know, like I'm, and maybe it was just my historical wording of it, but it really helped to kind of set that precedent ahead of time where it's, I'm not like challenging you or this idea, but it will help my team produce better work. If we understand like, why do you care about this? How are you like, how are you going to use it? Because we can, that gives us context and, you know, decisions that we might need to make throughout the process.

Team structure: centralized vs. embedded

Sure. Thank you, Rachel. So Rebecca, my question is really around actually building your team. Do you plan or have you thought through whether you're going to have one centralized group of data scientists that support the entire organization or your group, or do you think about maybe embedding data scientists in those functional groups so they get an idea of that, just that domain and work with it like that?

Yeah, that's a great question. My current thought about it, I'm planning on starting with a centralized data science team, mainly to just establish kind of like standards and processes around, you know, kind of like we talked about, like from a security perspective and some different kinds of methods that we use. So I'm planning on starting with a centralized team, but then my goal would be that as we kind of continue to build our self-service tools and up-level the scale of our business partners to kind of do some of their own like initial analyses that in the future. I really like the idea of the embedded model where the person sits under the data science team and can benefit from that technical mentorship and, you know, knowledge sharing resources, but then are aligned to a specific vertical because I think domain knowledge is just so important and that it can be hard when you're working on a bunch of different things to really get deep in that. So once we grow, I'm a big fan of the embedded model for just building that, like developing those team-specific relationships and that team, kind of team-specific domain knowledge.

Choosing Redshift and cloud infrastructure

I suppose it's a little bit more technical than the managerial and the team structuring and some of the organizational hurdles, but I noticed you mentioned that you went Redshift. So are you in a specific cloud environment like AWS right now and is that what led you to Redshift? And I was just, I don't have a lot of experience with Redshift. I was wondering what some of the trade-offs were with that versus other database technologies and whether or not you have a cloud hybrid or if that had something to do with it.

Yeah, that's a great question. The reason that we chose Redshift was because both myself and my data engineer came from Nordstrom and in Nordstrom, the database that we had used was Redshift in an AWS environment. So that's not to say that it's because it's just what we used before, but the benefit of that was speed and not having to learn sort of like a new SQL flavor or we've already had a lot of experience with like EC2 instances and moving data between S3 and Redshift and those kind of different types of processes. So I would say that the kind of main deciding factor was just that kind of speed and we were already familiar with it. So the learning curve, there wasn't really a learning curve and we could get to the work more quickly.

If that hadn't been the case, I think we probably would have done a bit more exploration on different tools, but that was really kind of like the driving factor of that decision.

Cool. If you're okay with that, I have one piggyback question. On your initial like upfront investments, so with getting your company to buy into the cost of the cloud environment, was that a challenge and was that an upfront investment or did you have to kind of prove your worth before they decided to hop on those costs?

Yeah. Our CTO is very much on board with the cloud technology, so that wasn't so much of a hard sell, but we actually were able to take advantage of like an AWS Redshift trial. So we got the first two months of our instance free and in that time we were able to produce some different sorts of analyses that really sort of drove value for the business. So then after that, you know, it was kind of an easy sell to say, you know, we need to continue to invest in this. And I also think that recently we were approved, applied for and approved as part of like an AWS startup that has, you know, like a certain amount of AWS credit. And so kind of taking advantage of those resources also helped to build the case.

Working with EMR data and unstructured data

Yeah, so we use Athena Health as our EMR. And one of the services that they offer is called Dataview, which is a snowflake, like database instance that they manage. And so that's actually made it like pretty, like easy for us to access that data because they're kind of like build like a database structure.

And so we're still, oh yeah, sorry. Yeah. Electronic medical record. So the data that we've accessed from that so far has mostly been structured data. And we actually, one of the kind of tools that we built was what we call like a PRM or patient relationship management. So you can kind of imagine like the concept of a CRM, but, you know, CRMs are pretty expensive. And so we built this sort of homegrown solution, leveraging data from Dataview and Snowflake has really nice Python connectors. And we were able to automate that. We use Daxter as our sort of like workflow automation tool. So that was sort of like a really big win to be able to automate like the pulling of appointment data and patient data sort of like into this kind of spreadsheet that then our clinical team can use. Whereas before they were having to manually export CSVs every night and copy and paste it.

And so I would say that we haven't really dealt too much with unstructured data from the EMR, but that's something we're still very much exploring because like for the note specifically, like there's a lot of good information there that we're going to want to extract around like social history questions and things like that. I would say when kind of earlier on, we explored some different partners like Particle Health and like Redox that were more about leveraging data from like the network of electronic health record systems. So like if I have a patient, you know, how can I get data from providers that they've already seen before in an automated way? And that's where I dealt with more of that like unstructured data because kind of the trial, we just worked with like a trial. We ended up not pursuing that at that point, but that was more of like the JSON like nested structure. And so that was definitely a learning curve for me kind of like working with that and trying to pull out what I needed, but we haven't had a like very big use case for working with the unstructured data yet.

Switching industries and continuous learning

I was curious, Rebecca, what it was like for you moving from a retail space at Nordstrom into your role now, and what was, yeah, what was that experience like switching industries?

Yeah, so I actually kind of have a bit of like a healthcare sandwich in my career, because I started out working in healthcare at a couple different health insurance companies, and so I had a lot of experience working with like medical claims data, pharmacy claims data, like enrollment data. I had worked on never building a risk adjustment model of my own, but like leveraging software, and kind of tangential to your question, but like one of my favorite memories of like early in my career was we used this risk adjustment software, I think it was called like DXCG, and I was asking questions to people in the department about, you know, how exactly does this work, you know, what it, like what inputs, like kind of might change the result, and no one could give me sort of like a firm answer, and so I realized that no one had read the documentation on this model, and that was actually an opportunity for me, because I printed, you know, printing was big at the time, so I printed out this like 30-page document and read it, and I actually became sort of the subject matter expert.

And then transitioning into retail, you know, I knew nothing about marketing at the time, but it's, I'm just a very like continuous learner, and I love to learn about new things, so I am kind of going back into healthcare at somewhere where, you know, I had kind of had some background in it, but a lot had changed in the time since I had left and gone back, so I think my attitude towards it is just being comfortable with uncertainty, and just being not necessarily attaching pride to knowing everything about a domain, and ask, and just being very open about asking questions and clear like, you know, I don't understand that, can you explain that to me, or I haven't seen that before, and just kind of having that courage to ask those questions made both of those transitions really helpful for me.

Making time for learning

That's really helpful, yeah, and I see Ravonda commented, always be learning is great life advice as well. How do you balance that in like a busy role where you just have so much going on, and so many tasks, and people asking you for things to still make time for learning?

I think I've gotten better at that over time. I, when I worked at Nordstrom, Nordstrom, they were very like upfront about carving out that time for self-development, and so we would actually include that as part of our like JIRA stories, so you know, if we have like, if there's 10 days in a sprint, we would only plan for like eight points worth of work to allow kind of that extra two days for meetings, and then carving out that self-development time, but then it becomes actually doing it, you know, like if I have my self-development time set up for Friday afternoon, do I just like stop working early and not do it, or like actually kind of do it?

So I think for me, it's kind of about like creating like a little bit of a plan about, you know, what is it that I want to learn, and like, how do I say this, I think for me, like sometimes for me, I get this idea that I have to sit down and like completely learn something in that session, and so if I think like, if I don't have three hours to go through this whole thing, then it's just going to be like not a good idea, but over time, I've learned where it's like, it's okay to start something and not finish it, and that's helped me more kind of learn new things where I'm like not setting super high expectations on myself, or I can just kind of play around with something.

I would say the other technique that I have is, I kind of did some self-reflection and realized which part of the day that I'm the most productive. I was, I'm kind of, I'm an introvert, and so I was, I used to have a lot of meetings in the morning, and so I would have my meetings from like 8 30 to like 12, and then I'd have the afternoon to work, but I would just be like so drained from that, and then I'm just like, I don't want to do anything, and really have to convince myself, and so once I realized that, I'm, I'm able to get a lot more done in the morning, and so I moved my, I had the ability to move my meetings to the afternoon, so then I would be able to have that concentrated time in the morning where I could be hands-on either doing a project, or kind of exploring something new, and then having my meetings during that time where I've already kind of, you know, I did what I wanted to do today, and now I can focus on more of that communication aspect.

Tips for breaking into data science

The best advice that was ever given to me was that data science is a contact sport, and what they mean by that is that the best way to learn is by doing, and you know, my recommendation for trying to kind of break into the field would be, especially if you know which domain, you know, find a data set that's on the internet. Kaggle is a really great resource for public data sets, and just do something with it, right, like it can be as simple as an exploratory data analysis. You can take it further and do some kind of like predictive model or like an inference type of task, and then put that on a GitHub profile, and if you kind of want to go above and beyond, like I have a personal website where I, you know, it's just a Google site, so it's not any fancy web development, but I write about the work that I do, and I link it to my GitHub, and I have that on my resume, so I found that that really helped to, has helped me in my career, and it gives me something to talk about in an interview.

The best advice that was ever given to me was that data science is a contact sport, and what they mean by that is that the best way to learn is by doing.

Management, delegation, and team culture

You know, to be completely honest, I struggled when I became a manager for the first time. I was a really good IC, like not to toot my own horn, but like I was a really good IC, and when I first became a data science manager at Nordstrom, like it was really hard. Delegations specifically, like I would have an ask come in and you know maybe it was like really quick turnaround for some like marketing fire drill, and I would have the thought like it's just quicker if I do it, like I can just do it, and I thought that I was helping my team by kind of saving them from doing that, but then what I realized is that one, I was actually not giving them an opportunity to shine and build those relationships and kind of have that, you know, like I did this thing that was really impactful, and then two, I got super burnt out.

So once I was realizing that, I started looking at different sorts of like management books, like how can I kind of build that skill set, and one book that I really liked was called like the Hands-Off Manager. I can't remember who it's by, but I can like share a link to it, and that really helped me because I didn't want to be a manager that was very like micromanaging, but I also, you know, you have to hold people accountable and get things done, so I kind of was having that balance between you know checking in and making sure things are moving along with not wanting to be micromanaging, so that book in particular really sort of helped with like changing my perspective on how I can like support my team, and you know like say like one of my team members is struggling or like missing deadlines, it's instead of like why are you doing that, I can't believe you're missing deadlines, it's more about like you know I know that you're capable of this, so like what's going on and kind of having that different change in perspective.

Fostering curiosity and a learning culture

Yeah, that's a good question. In my current role, I haven't had to really encourage it because the person that I have on my team is naturally curious and I've worked with them for a while, so it's, I would say it's less about encouraging and more just we have casual conversations about you know what are we kind of interested in. I would say maybe the thing that I do with my team that I like is that we have like our team meeting on Friday and we do like different Fs, so we do like F'd up Friday, so like here's something that I F'd up this week to kind of take away, kind of take away that fear of like making mistakes, you know like let's just laugh about it and actually share what you learned from it. Then there's fine work Friday, so it's actually you know celebrating like I really liked that documentation I wrote because it was super awesome and helpful. Then the third one is found out Friday, so like what's something that you learned this week and so having that be kind of part of our like weekly team meeting, I think kind of you know it becomes part of the dialogue, learning becomes part of the dialogue of the team especially on a regular basis.

In my role at Nordstrom, I worked with, I had a little bit more junior members on my team and there was this one team member that I had in particular who she was super smart and like very like a very good analyst but her coding wasn't the strongest and so I kind of just like told her that I believed in her and that like I knew that she could learn it and kind of just that like belief and encouragement like kind of helped her not be afraid to try it. I feel like there's a lot of times like fear of failure of trying something new, so I'm not sure if I'm answering your question in a very direct way but I would say it's more like building, creating space for people to not know everything and kind of having the team be about you know I learned something new or I learned that something that I thought I knew was incorrect.

Yeah thank you, that's very interesting to hear about the field of sharing meetings because I've heard about you know meetings where people share about their achievement but not necessarily openly talk about their effort. So very interesting that you know you can encourage people to openly talk about that then everyone can learn from the mistakes.

I was just going to say to kind of expand on that like I try to take that culture also to my to my business stakeholders as well. I've had a lot of experiences in the past where there's some campaign or something that like my business stakeholders did that ended up not being a success and then there's a lot of kind of like weird feelings and your data's wrong and you know kind of like push it under the rug and so I try to also encourage that like concept of you know it's like not scaling something that didn't work is like a good thing right like even though like you tried something and this particular experiment wasn't successful like you avoided scaling it to our entire customer base so like you know let's move on to the next experiment kind of encouraging more of that you know failing faster so that we can kind of iterate through things and figure out what does work because most of the time the things that we're going to do like probably aren't going to be successful.

Becoming a manager and career paths

Sure I guess I have two questions so the first one is did you always know that you wanted to be a manager and then also your Friday's idea is absolutely fantastic and I'd be curious to know more on how you landed on that idea were you inspired by like either you know poor meetings or was there someone that really inspired you that you know you got that meeting idea from or just how how'd you come up with it?

Yeah I would say no I didn't always know that I wanted to be a manager I would say it kind of like came out like naturally I would find that I'm always that person in class that like is asking questions and like makes people stay longer because they're like asking so many questions and people probably get really annoyed at me for that but it was kind of like I really liked the technical work I was doing but the part that I really liked is like helping others and sharing my knowledge and like building relationships with with stakeholders and so kind of those things kind of put me in a position where you know a reorganization was happening and there was this new manager role being created and I kind of I like put my hat in the ring and I said I you know I want to do this and I think I'm qualified for xyz reasons and kind of Sheryl Sandberg my way into that job so you know just to try it and I believe that career paths are like not necessarily linear so even though you know right now like you know I'm the head of data science like I can see myself in my career being a like principal data scientist at some point so I'm kind of not like making like a firm commitment to a certain path but becoming a manager actually gave me really great perspective on my work as an IC because you know is that like imposter syndrome like does my manager think I'm doing enough like how how am I working how does my work compare to other people's and being a manager really helped me sort of gain that perspective I was okay yeah like I'm pretty good.

And then as far as like the found out uh I can't remember I mean I think it was more just like I'm a big fan of like puns and alliteration and um tasteful use of the f word so I would say that's how it kind of came together and it's just working in a small environment um and knowing the people on my team really well I kind of was like okay I can I can get away with this.

Thank you so much Rebecca this is great and thank you all for all the great questions too. But Rebecca I really appreciate you sharing your experience and insights with us this was awesome yeah thank you so much for having me and I've got to drop so thank you so much for the great conversation.