Welcome to the eighth episode in our “Meet the Mentor” series where we get to know Flatiron School mentors in an interview-style conversation. Whether you’re just starting out or looking to level up, these stories are packed with practical advice, encouragement, and insights to help you navigate your own path in tech. Each article includes a mentor snapshot + links to follow their work, their video interview, and their Q&A transcript with links to any references.
Follow along and discover the people shaping the future of tech, one student at a time!
Meet Frances Cue
What began as a career in healthcare eventually led her to discover her passion for data and technology. She works as an AI Engineer and Data Scientist at a major hospital organization, where she focuses on building and optimizing LLMs, RAG Engine APIs, document classification systems, and Vertex AI search and recommendation engines.
In her Meet the Mentor interview, she shares how she pivoted from nursing to tech and why she believes that asking questions and embracing failure are key to growth. Her story is a powerful reminder that it’s never too late to pivot, keep learning, and find success in a completely new field.
Snapshot
Current Job Title: Data Scientist / AI Engineer and Flatiron facilitator
Current Employer: Hartford HealthCare
Past Employers: Pager
Experience: 6 years in Data Science
Technical/Professional Skills: Natural Language Processing (NLP), Large Language Models (LLMs), data mining, data analysis, statistical modeling, Python, R, PyTorch, NumPy, deep learning, Vertex, Application Programming Interfaces (API), Structured Query Language (SQL)
Teaching/Mentoring Experience: “I have mentored for free before, just as a volunteer for smaller companies. I’ve always wanted to share my experience with people, especially with people that are coming from non-technical into technical space.”
Words of Wisdom: “Do not disregard the soft skills that you have. Even if you’re coming from a non-technical background, believe me, those soft skills are transferable.”
Favorite Part of Your Job: “Keeping up with new trending things and learning new things.”
Meet the Mentor Interview with Frances Cue
Q&A Transcript
Introduction: Who are you and what do you do now?
- Can you tell us a bit about yourself and what you’re working on these days?
- My name is Frances. I am an AI Engineer/Data Scientist. I currently work for a large hospital organization in Connecticut. Most of the work that I’m doing right now is based on LLMs, working on RAG Engine APIs, doing some cool stuff with document classification, as well as implementing Vertex AI search engines and recommendation engines. Typically, the day starts with if there’s a new request from a stakeholder or somebody that needs something that relates to an AI task. There’s typically a process that we should follow, and then we pretty much triage that request and plan out the day, whether it’s something that’s urgent that needs to happen immediately or something that could wait in the backlog. And then typically, so in an engineering environment, we follow the agile process. So you’re going to hear this a lot, but maybe user stories or tickets for a set of two-week sprints. The goal is that you finish your tasks, your sprint tickets within the two-week timeframe. Within your tickets, you prioritize which ones are the highest priorities and you work on that first. You just work through those tickets during the sprint. If you follow the agile process, that’s what we do. And then a lot of times, a typical daily task would be communicating with stakeholders, giving them updates, asking follow-up questions about the work that needs to be done, and communicating with stakeholders. And you have your typical engineering meetings, which is usually either a stand-up, and a stand-up is just some people even call it, I think, a daily huddle. And it could be like a 15-30 minute meeting. And the purpose of that meeting is to update everyone if you have any blockers for your tickets. So if there’s something that’s holding you back from completing a task, you verbalize it to the team, you ask them for help if you need help, and also just to update everyone in the team what your current status is with regards to a ticket, whether it’s behind, is it going to be completed for this current sprint, or do you need more time, is it going to roll over things like that. So that’s just one of the meetings that I could think about that I have to attend daily. And then occasionally, another one is having one-on-ones with my manager. It’s not a daily thing, more like a weekly thing. It’s just like a check-in meeting to make sure that if I have any questions with regards to the work, I could speak with my manager about it. If there’s anything I need, I need him to help me prioritize, things like that. Whatever meetings you don’t have for the day, then that time is set to help you complete your task. That’s what it’s for: coding tasks, data analysis tasks, and so on. So as an AI engineer, a lot of my tasks is related to coding and making sure that our infrastructure for the AI engines that we have built is running smoothly, making sure that we have unit tests, integration tests running smoothly. Sometimes, there’s emergencies. Sometimes there’s something that’s not working in production that you need to fix right away. That rarely happens. Hopefully it doesn’t happen to anybody. But it does happen.
- Has your role changed in the last few years because of AI?
- I’ve been a data scientist for about almost 6 years. So I started as a data scientist right when COVID started. And it was the most because I used to be, believe it or not, in my past life, I used to be a nurse. And like the students in Flatiron, I used to be non-technical. And then I transitioned over into this technical role at the beginning of COVID, so that was very stressful because I remember my first deployment. That was the first day that the company told us not to come back to the office in New York City. Wow, yes, I was so nervous because I had to deploy from home and remotely. But thank God there were other engineers that helped me. So anyway, I’ve been doing this for 6 years and a lot has changed. I’m seeing a lot of different things that have changed. When I first started doing this, because the company that I worked for before knew that this was my first data scientist role, they gave me more of a data analytics role. My title was data scientist, but the tasks really were focused more on data analysts. So, for example, SQL queries, creating dashboards, things like that, speaking with stakeholders, providing insights, those things. And then I feel like ever since I started, there has been so much growth in the AI space. So up until like 3 years ago, I would say the focus has been more on like data analytics and maybe some classical machine learning. But, since ChatGPT came out and there’s been an LLM boom, the tasks have shifted more toward NLP, natural language processing. And it makes sense for a lot of companies, right? So my previous company, we did have conversational data, like messages between customers and our agents. So the big thing was, how do we summarize this data? And then boom, ChatGPT came out. Gemini came out. All of these nice LLMs came out. And you don’t have to tokenize. There’s a lot of data pre-processing that you don’t have to do compared with the traditional machine learning that we’re used to seeing in NLP, right? So the NLP space has changed so much over the past 3 years. It’s incredible. And I just, I can’t wait to see where else it takes us, right?
- With all of the new AI developments, are there any key skills that you think are crucial to the workplace now?
- This might sound surprising to some, but, and I always tell this to my students, do not disregard the soft skills that you have. Even if you’re coming from a non-technical background, believe me, those soft skills are transferable. I’ve had to do the same thing, right? I’ve had to transfer from a nursing role into this very technical space. And I do lean on a lot of my soft skills. And by soft skills, I mean like communication, making sure that things are clear between the stakeholders, between you and your teammates. Really, communication is key. Because you could be the most technical person. You could be a wizard. You could do all these things. But if you do not communicate clearly, I can guarantee that the project, whatever you’re working on, is not going to go well. So you really have to have that communication aspect. I do want to say that because AI is here, there are a a lot of companies now where I’m noticing a trend. And it’s not scientific at all, and this is just from my observation. But I’m noticing a trend that companies would still ask you to code, but they are asking you how to use AI to help you code, right? So how do you use, how do you utilize this tool to help you code? And do you know what you’re checking for in the AI output? So yeah, AI is great for help. I mean, it has fast-tracked my processes at work like 10 times faster. It’s amazing. But you still have to learn how to interpret that output and make sure that it’s not hallucinating or it’s not giving you anything that’s weird or that’s gonna throw off your production environment. So that is one thing to watch out for. Maybe that’s a tip that I give to students: when you interview with a company, ask them how they are with AI tools. How are they looking at AI tools? Are they looking at AI tools as something that could help you process things faster and become more efficient, or is it something that they prevent you from using at work? It would just be interesting to find out where their perspective is. Because AI is here. It’s not going anywhere. It’s already here. You might as well use it, but you have to know how to use it properly and responsibly.
Career Journey: How did you get here?
- Can you walk us through your career journey?
- I started nursing. This will give away my age too. That’s when I graduated from nursing school. So I first started nursing with an associate’s degree. And the areas within nursing that I worked in are mostly ER, some medical-surgical floor. I also did what’s called a float nursing, which is when they ship you to any department in the hospital when there’s a need. So you kind of have to be ready. You arrive for your shift, and you don’t know where you’re going. So I did that a little bit. I did some psych nursing. I did all kinds of nursing, even cath lab. So I did that for almost 13 years. The last nursing role that I had was telemedicine nursing for the same company that hired me to become a data scientist. So what happened there is that I had graduated from my master’s in public health. During the curriculum, I graduated from UMass Amherst. In the curriculum, we had to take the statistics and epidemiology and some data. It taught me data collection, data analysis. I’m like, “I really like this stuff. It’s really interesting. I really like it.” And then so I graduated. And in the same time, my company back then said that there is a big opportunity if you want to transfer, learn about other departments. And I was working for them already as a telemedicine nurse. If you wanted to shadow or look into a different department, they were going to support your transition. So I said, wow, this is a big deal because I’ve always been curious as to what’s going on on that side other side of the office where they always look like crazy over there. I don’t know what they’re doing, but I’m really interested. I want to know what they’re doing. So, yeah, I shadowed a few engineers, and I was really interested with what the data team was working on, creating visualizations, creating metrics and things like that. I was like, “Okay, that’s really interesting. I want to actually try to move into that department.” And my employer at the time was very supportive. They actually helped me get into a bootcamp. I did bootcamp for about 6 or 7 months for data science. And then once that was done, I presented them with the certificate and they were like, “Okay, that’s it. You could move over.” And that was it. That’s how it started. And then, yeah, so that happened in February, and by March, we were all working remotely. I still can’t believe how it happened because I know a lot of people, not just in the nursing space, but in whatever industry they’re working in. They say, “Oh, I want to do this. I want to do that, but how do I do it? It’s hard to transition. I’m too old. I can’t learn.” No, don’t say that. Those are excuses. There is a way. And believe me, if I could do it, because if I could do it, anybody could do it. Because I had a kid, like, how old was she? Maybe she was like 6 or something. Yeah, she was around 6 when I transitioned and when I was in school. You could do it. It’s hard. It’s probably going to be the hardest thing you will have to do. But if that’s what you really want to do, go for it. Go for it. Don’t let excuses stop you.
- What’s your perspective on tech education today? What is the value of a bootcamp compared to other options like teaching yourself or getting a masters, associates, or 4-year degree?
- I’m all for bootcamp. I don’t know if you know this, but I’m all for bootcamp. That’s how I got in, you know, that this is how I got in, in the tech industry. And I’m so thankful for that because, yeah, I wasn’t in debt for a four-year college degree at that time. I think bootcamp is enough to put you into that entry-level space. Once you have that first step, that foot inside the door, you learn as much as you can from this entry-level job, right? And if you feel like you need more education, like you’re not getting enough education, you could always go back and get a master’s degree. I actually did go back and get a master’s degree from UC Berkeley. But just to put one foot inside that door, I do believe that bootcamp is enough. Bootcamp and a lot of perseverance. You will need it. It’s doable. So I like the bootcamp because it gives you just enough basic concepts for you to be able to understand, even at the basic level, right? Because even in regular school, you’re not really going to learn everything. They’ll teach you some courses, but the true learning really happens at work. That has been my experience, even with my nursing career and with my data science career. You could learn as much as you can from school, but the true learning really happens when you’re doing it and when you are stressed.
Lessons Learned: What have you learned along the way?
- What’s one lesson or insight from your career that’s stuck with you?
- I always say this to my students: do not be afraid to ask questions. Please ask questions. So it can be, when you’re new and you don’t know anything like going in, it’s really intimidating. And then you have all these senior engineers, senior data scientists that are around you. Use them, that’s what they’re for. They’re there to help you grow, right? So, if you don’t ask questions, they don’t know that you’re not understanding something. So make sure you ask as many questions as you want and don’t be afraid, thinking like, “Oh my god, they’re gonna think I’m stupid or I don’t know what I’m doing.” You’re new. You don’t know what you’re doing. Just say it. There’s nothing embarrassing about it. And I used to, I made the same mistake, right? I was very, very intimidated. And it was like, I was like the only female there. The only female. So that’s even more interesting because it was like a total switch, right? I switched from the nursing career to the tech industry and it’s like 90% male. So I’m like, “Oh, it is different.” Like the politics are different. The scenarios are different. But one thing I could say is if you don’t know, just ask the question. Use your manager, make sure that you have a copy of the career ladder from your employer because that’s how you know how you can level up. They could tell you, “Okay, for level one engineers, this is what’s expected of you. But to level up, if you want to level up to level two, then these are the things that we’re expecting from you.” Make sure you have that from day one. You’re aware of what that is so that you have a clear goal, a clear path on how to move forward. So you don’t feel stagnant because sometimes it happens, right? I mean, there are some things that engineers do, that data scientists do that could feel a little bit more routine. So make sure you have a copy of that, how to level up at work. How do you get promoted? How do you move on to the next level? So ask questions and get a copy of that career roadmap.
- Was there a moment where you faced a major challenge or failure, and how did you grow from it?
- When I was first starting out, we were in such a small data science team, and the company that I was in was a startup. So we were kind of trying to do things ourselves, but then nobody was a true expert in the data science field. So it was really hard for us to deploy machine learning models and actually get good learnings from the deployment. And so a lot of the times we would have a product for 3 months, we would work on it, get your evaluation metrics as the stakeholders, but at the end, it doesn’t go through. Like you present it, you’re so proud that you made something, right? I once made a model to predict how many customers you would get in a daily, weekly, monthly timeline, right? I was so proud of that because I worked on it for 3 months, but in the end, they didn’t really see the value of it. And then I realized that I did not speak with the right people when I was pitching this, and so it kind of lost momentum. And now it’s just in the backlog somewhere. I don’t know if somebody will ever pick that up. But yeah, I would say because I failed from that project, that helped me build for my next project. Then I kind of knew who to speak to, like, Okay, this time that plan didn’t work out, then this time I’ll try this other plan. I have a new project to pitch. So with each failure, you will fail, it’s inevitable. I’ve never met a data scientist that said “Oh my god, my projects are all 100% accepted, now it’s all in production.” You know, that’s just not reality. There will be projects that just don’t go through, But still be proud of it, because if you have put some time to work on it, you’re always going to learn something from it. Always. Every project gives you something back, even though it doesn’t get approved or doesn’t get deployed in production. There are always learnings from it. So yeah, don’t be afraid. Don’t be afraid to fail, because it will happen. It’s hard when it happens, but sometimes it’s necessary for you to grow and move on to the next phase.
Mentorship: Why did you decide to become a mentor?
- What connected you with this at Flatiron School?
- I think I started like late summer. I may have seen. Oh, you know what? I’m going to share a really interesting story. So back in 2019, when I was looking for bootcamps, I actually applied to Flatiron. And I failed. I failed the exam. I honestly don’t remember but I think there was a written exam or there was a mentor that would screen you and tell you whether you’re prepared to enter. And I failed that exam. But I’m still here, guys. Like, see? Don’t give up. It’s going to happen. Don’t give up. So that was my very first time I heard of Flatiron was through that. I also know of some engineers that actually are graduates from Flatiron from my previous role. So then fast forward many, many years later, I think I saw LinkedIn post on how you’re recruiting facilitators. So I applied and I got in touch with one of the admins. And yeah, here I am. I have mentored for free before, just as a volunteer for smaller companies. So I’ve always wanted to, maybe it’s because I talk too much. I don’t know, but I’ve always wanted to share that experience with people, especially with people that are coming from non-technical into technical space, because when I was first doing it, I didn’t have that support system. I was telling my nurse friends like, “Oh my god, guys, l’m going to this bootcamp.” They’re like, “What? What is that? What is statistics?” So, I forgot what I was saying, but anyway, I’ve always wanted to mentor people, and I found the the Flatiron curriculum to be robust when I first started, and then I really enjoy teaching the capstones, teaching the students like “In the real world, this is what really happens” things like that.
- What inspired you to become a mentor?
- Other than I like to help teach people, I want to share my knowledge and share my skills. I also think that the best way to learn is to teach. I’ve always been a school nerd, and I love learning and relearning. So my husband said that I am going to be in school forever until the day I die. Because I went through nursing school. I got my associate’s degree, then I got my bachelor’s degree, and I got my master’s degree in public health. I went to a bootcamp, and then I got my master’s degree at UC Berkeley. So then when he found out that I am facilitating, he’s like, “Oh, thank God. You’re done being a student. Now be a teacher.” And I’m like, “Yeah, I love it because I get to talk about my experience.” At the same time, I’m still learning too. Like sometimes I’ve had students that are brilliant and they have math backgrounds, and they’re teaching me about time series like, oh my god this is amazing. I’m learning from my student, this is great. So yeah, I love that I am also learning from my students, and I think it’s a good back and forth.
- Was there someone who influenced your career path and who (knowingly or unknowingly) mentored you?
- Definitely my, so from my first employer as a data scientist, before I even transitioned into the data science team, I knew the data science manager, and he was already my friend. So that was already biased because he was already my friend. But he really encouraged to go for it. I would share with him frustrations through the bootcamp when I was going through the bootcamp. At one point, I’m like, “You know what? I can’t do this anymore. Maybe it’s just not meant for me. But he really helped me i didn’t cheat okay but he really helped me he really helped me like just with that emotional support. Sometimes you really need that. And he also would clarify certain concepts. So he was more of a machine learning engineer, and I feel like we kind of learned data science concepts together. So, you know, there are overlaps between a machine learning engineer and a data scientist, but I feel like he is the same as me, where he likes to teach so he could learn. So he was actually a very big influence on me because until this day, I still use the concepts and the knowledge that I learned from him, both soft skills and technical skills. I still use it. And I would say, what would he do in this situation, right? So yeah, he was the one that really helped me get started and I’m very, very thankful to have found somebody like that.
- What has been the most rewarding part of mentoring students so far?
- Like I said before, I love to talk. So I love to talk, and I love sharing my experience. So that, for me alone, is rewarding enough. I really like seeing people grow, and I like that they come back to me, and I love it when I give them the lecture and when they hand me an assignment, and then I notice that they actually took what I said and applied it to the assignment. I get like, “Oh my God, this is so nice. This is so cool. I get to influence someone.” It’s amazing. And their minds are so brilliant. It’s just amazing how with enough perseverance and enough studying, you could actually do these things. And it’s amazing to see people do that, to see people pivot from one area to another.
- How do you keep your students motivated?
- I hope they don’t get bored, honestly, when I talk a lot. I always try to make sure that if they need help with anything, like if there’s a concept, because sometimes I feel like if you don’t understand something and you feel like that’s a blocker, then you just don’t know how to move past it. I always tell my students, “If you need help, do not hesitate to reach out. Even if it’s, you know, email or however you want to reach out, just reach out. This is what we’re here for.” We could go through the concepts again if it’s something that you don’t understand or you just need extra help with. No problem. I also like to facilitate having students talk to each other because I feel that bond that is created during a bootcamp is really strong. I still have friends from my bootcamp back in 2020 that I still talk to until this day. Because you go through this really tough shared experience, I feel like that creates a strong bond. So I do encourage them to talk to one another. Especially for Capstone, like, “what do you think could be improved? What do you think, what did this person do that was really good?” Just get feedback from one another.
- If you could give one piece of advice to someone just starting out in tech, what would it be?
- Just go for it. If this is what you really want to do, don’t make excuses. I did that myself too, like I was thinking, oh you know what, I’m not smart enough, I don’t understand math, I don’t understand statistics. If this is what you want to do, just go for it and it will work for you, right? You will make it work for you. It’s probably easier said than done, but there are ways about it. If you need to reach out to, if you’re having trouble trying to figure out whether this is the right thing for you, what I actually did, and this is a tip from my data science manager friend, he told me to reach out to strangers. I think LinkedIn was a thing back then too. And then he just asked me, just ask them what their experience is. And then it could really help you figure out whether this is something that you want to do. And I did. I felt so stupid doing that. Like, “Oh my God, why am I doing this? Why am I reaching out to these strangers?” But no, people actually respond. You’d be surprised. People actually respond. I remember I reached out to a blogger and he was writing about, he had like BERT models, that’s what his main topic is always about, like BERT models, embeddings, vectors. And I reached out to him because I was reading his articles. And I was like, “How do you like it?” And then he gave me his perspective. And then I realized like, “Okay, you know what? Yeah, I think this is something I really want to do.” So don’t be afraid to talk to people network reach out to people that could really help you decide and just go for it.
Future Focus: Where do you want to go next?
- What’s something new you’re learning or exploring right now, and why does it excite you?
- I didn’t think about my future goals. So there’s always, so with LLMs, I feel like every day there’s a new paper, there’s a new benchmark out there that you need to read, and you need to understand a new concept every day. So for now, my immediate future goals would just be to understand anything that is emerging because it’s very fast-paced, extremely fast-paced. So as data scientists, we have to keep up with that technology because I mean, classical machine learning, of course, is a big thing still, right? But there’s definitely the trend moving more towards these new AI concepts. So I’m just trying to keep up with all of these new trending things. And there’s always new architectures, new things that you need to learn, even if you don’t implement it, right? But just be aware because you never know when you might actually need to use that at work.
- Are there any projects that you’re working on right now that you’re particularly passionate about?
- There are projects. But it’s actually, this is going to sound weird, but they’re not data science stuff. Like for work, it’s more of like this is why I’m saying you have to learn new things because sometimes you get thrown things that are not really within your space but you have to to pick it up. So right now, I am helping with our DevOps, with our CI/CD pipelines, and doing some container work. I know that sounds not data science at all, but for me, it’s really interesting because I’m learning. It’s new to me, right? It’s not something that I normally do, but I’m learning from it, so I’ll take it. So yeah, that’s very interesting for me. And my other current project is classical machine learning, which is document classification. So we have a set of of documents that just need to be classified into certain categories, and I actually used classical machine learning for that. Well, I mean classical, we use BERT embeddings, it’s classical now, but we used BERT as a baseline and then converted, and then later on, probably going to use LLMs to see if we could use LLMs to classify those documents.
Lightening Round Questions
- What’s something you’re listening to or reading right now? (It can be any genre and can be a book, audiobook, or podcast.)
- I always read. So I have articles that get sent to me from various blogs. So like any data science newsletters, that’s one way I definitely, that’s like my light morning reading. Yeah, I can’t give a specific article, but that’s pretty much what I do every morning. These are ones from Substack. I don’t remember the name of it. There’s one called The Rundown. I like that one because I feel like it gives you the rundown, like the major ones. And then I still read, this is going to sound so nerdy, but I read epidemiology papers from time to time because of my public health background.
- What’s one product or tool you’re into right now?
- I just found this out the other day. It’s called Napkin. I think it’s Napkin.io. I don’t know if you’re familiar. It’s a text-to-image, but it’s like a text-to-graph generator. And I don’t work for them. I’m just saying that this tool is amazing because sometimes I need to make diagrams, and I hate making diagrams. For example, for architecture work, you need to make diagrams a lot, so I found it really helpful. You just type in your thoughts like step one, step two, step three, and then it automates it for you and it creates this nice, beautiful diagram.
- What date does your next cohort start?
- I just started a new one yesterday. Also Capstone, 15 weeks.
- What made you smile this week?
- My cat was lost for a while, and then she came back. We found her, and then she got lost again this week. But it’s okay, I think now she knows how to come back on her own, and I didn’t have to go searching for her. Now she’s back, so that really made me smile. I didn’t have to go crazy looking for her.
