Skip to main content

[DataFramed Careers Series #1] Launching a Data Career in 2022

Today is the start of a four-day careers series covering breaking into data science in 2022. With so so much demand for data jobs today, we wanted to demystify the ins and outs of accelerating a career in data. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of standing out from the crowd in the job hunt.
May 2022  · 277 min read

Adel Nehme, the host of DataFramed, the DataCamp podcast, recently interviewed Sadie St. Lawrence who is the Founder and CEO of Women in Data.

Introducing Sadie St. Lawrence

Sadie. It's great to have you on the show.

Sadie St. Lawrence: Oh, it's great to be here. Thanks so much.
 
Adel Nehme: I'm super excited to talk to you about, breaking into data science today. How aspiring data practitioners need to think about their career paths, best practices to stand out in a competitive space, your work leading women in data, and much more, but before, can you give us a bit of a background about yourself and how you got into the data?
 
Sadie St. Lawrence: Yeah, I'd love to share. So I came into data career in 2014. At the time I was working in a neuroscience lab with the plans to go and get my PhD. Science and soon revise. I really love the analysis side of things and didn't so much enjoy taking care of rats. And then unfortunately having to kill my rats at the end of using them.

And that was a quite a bit discouraging. And so. What I did was I stepped back and looked and said, what parts of my job do I really love? And what parts could I do with that? And what I was left it again was the analysis, the scientific method. And I was lucky enough to find the term data science. And when I found term data science or a Google search, I immediately latched on.

It was just like, yes, this is me. This is like everything that I want to do and want to be. I quit my job at the lab within the next few days and was like, okay, I just need to get a job working with data in some way. And so I started off as a research analyst, then started taking some courses to some MOOCs, realized I've really loved it and then decided to go and get my masters in the field as well.

And that started just a really exciting time. Where I was able to be a research analyst and then an analytics engineer and then a data scientist. And then I was able to lead a data science team, and then I've been into AI strategy. So I've had a really fun journey in this space. And then now, today I get to do what I love the most, which is to lead with the new data help coach others and build pathways for diverse audiences to get into this.

Adel Nehme: I love this story and I'm very excited to unpack a lot more of your journey. But there's definitely a lot to discuss today when it comes to breaking into data science. When I first . Joined the industry, , and that wasn't necessarily that long ago, you'd only see two main ones.

Right to hire for four data analyst or data scientist. and this is in some sense still true today, but we see a lot more variations specialization between these roles. we have the emergence of hybrid roles, like financial analysts that require more data skills, marketing ops, biz ops, and even business intelligence orals.

So as an educator, you're someone who has been embedded in this space for a long time. What do you think are the different types of data careers available for aspiring practitioners? Looking to break into data?

Sadie St. Lawrence: Great question because a lot has changed since 2014 at this phase when I first entered. So on the positive side, there are so many more resources for learning today. So when I was getting into the space, I'm in the U S. There are only five universities even offering master's degree at the time. So I just share that because if someone has been interested in getting a master's or, going through formal education, they'll know the plethora of resources and options available, let alone the courses are available through.

I don't even know if data camp existed at the time that are available through private and online education that exist as well. So I think it's really exciting that there's so many resources. But the hard part now is today's exactly what you mentioned. There's so many more jobs in this space and now they're getting a little bit more specialized.

So one of the things that I see is people are looking not just for a data scientist or analyst or data engineer, but they're looking for someone who has those skills and also. The industry skills or the business function skills as well. So as you mentioned, it's really important for people not to just say they want to be a data scientist, but what type, right.

Do you want to be a product data scientist? Do you want to be a financial data scientists? Do you want to work at a consumer goods company, like really narrow in, on industry you care about? Like healthcare is I think a really exciting place to be because. Well, why don't we have seen how important health is in the last two years of the pandemic and how important data is in this space and the models that we've built probably lives they can save.

So I would say, make sure if you're looking to get into this space, you're not only learning those technical skills, but you're learning those business skills as well. Whether it be from an industry or a function from a job. Means is it a marketing side? Is it a financial, an operation side of things? I think if you put those two composts together, you have a really clear cramps to write that will make it a lot easier to be able to break into the field.

Adel Nehme: That's really great. And in some sense, this creates an easier career pathway into data science, because if you're a marketer, a financial analyst or someone who has the subject expertise. You just need the technical expertise on top of that to break into data science.
 
Sadie St. Lawrence: Exactly. And it also really helps to distinguish you as well as with the crowd. So it's just a, win-win all girl.
Adel Nehme: How do you assess the importance or the trade-off to a certain extent between these business skills and these hard skills, what do you think are the most important skills in that mix?
 
Sadie St. Lawrence: Oh, yeah, That's a hard question, right? Because both are important. And so that doesn't really answer your question of one versus the other. But I would tell people though, is if you need both of them, how do you balance living both of them on your learning journey? And gee GI like to do this for people.

Is pick your way, but know your ocean and what does that mean? Well, the ocean is a very vast place, right? And that's a lot of times with uh, data career can feel like, even if you're just focusing on data science, there's all these skills you need to learn for data cleaning and data handling. And data governance and data engineering.

And then you get into the analysis side and the machine learning side and the data visualization side and the communicating all those skills. So that's enough just in of itself on the technical side of things. And now you're saying to me, you're asking me to also learn these business skills. Like how do I do it all?

And that's where the knowing your wave comes in, right. Of having a really clear vision for where he wants to be. And so I'd say on the business side of things, Really make sure you're. Taking the time to talk to people who already work in that hole, making sure you're not just reading the technical articles of what's going on in business, but also just the broader business scope of things.

And so for me, one of the ways I like to understand businesses is to read through their website, but more importantly, if they're a public company read through their financials. And so I think that's the beauty of. Uh, public company is when you look at their financial statements, you really get a, insight view into how do they make money?

How do they use money? What are the products of trying to sell? And at the end of the day, understanding your business is quite simple, right? It's easy. It's, how do we make money so that we can continue to grow and support our employees and support the customers that we're servicing.

Importance of Data Storytelling

Adel Nehme: And you mentioned here something in your answer around communicating your brand or communicating the technical skills that you have. How important are communication skills and data storytelling skills, as a means to break into data science and to jump out and stand out from the crowd.
 
Sadie St. Lawrence: The analogy I like to use is like a music box, So if you've ever seen a music box, if it's closed and just sitting on the table, you never actually get to hear what the beautiful sound is inside of it. that's similar in terms of data scientists, not having communication skills, they may have these amazing skills, but they're all locked in this box and then no one ever.

And so you have to open the box and how do you open the box? You open the box by being able to tell those stories and to communicate those skills. So, it's really up to you, right? Do you want people to hear your story and to hear your amazing skills and ability? Well, then you're going to need the communication skills so that you can open the box neck.

Adel Nehme: That's great. And you're someone who's, in my opinion, a great communicator. And that sits at the intersection of like technical skills and communication. How did you grow your communication skills over time? I know there's some form of it that is innate, but I'm sure you've gotten better at it over time.

What was the way that you've been able to get better?

Sadie St. Lawrence: I would say, take every opportunity to use those communication skills. So I know early on in my career, it can be daunting to say, yes, I'll need this presentation or I'll present a portion of this, right. But one take any opportunity that presents itself. And also if there aren't any opportunities that present itself, volunteer yourself to be able to lead that communication.

So it really is a matter of practice. The other option. As we live in a digital world and we have these great tools of social media through Twitter or LinkedIn that readily available for all of us to just start to write and communicate. And that is such a great option in terms of. One practicing, but more importantly, as you go through that practice of communicating, it also helps you to refine your process in your work.

So I would say practice makes perfect and take every opportunity and seek out opportunities to communicate the great work you do.

Adel Nehme: That's awesome. I couldn't agree more, especially on taking that leap of faith and kind of going for the presentation whenever you get the chance. So moving in, in our chat on our chat, I think there's never been more interest in a data science career as a career path today. there are a lot more learning resources, as you said, a lot more organizations opening up data science department.

More data skills and kind of combination of business skills and data skills that are needed. This means that the demand for data roles is higher, but the competition is also getting higher. so what would you think are top principles for standing out in the job market today for any aspiring practice?

Sadie St. Lawrence: First I would say, I think it's great. There's this momentum and so much interest in the data. The forecast of the opportunity in this space is looking really, really well. So the world economic forum produces this job report that predicts the top jobs over the next five years. And so in 2020 predicted again for the next five years.

So that goes through 2025 and in the top 10, three of those top 10 jobs. We're all data, careers, machine learning, engineer, data scientists, data analysts. And I think it was a big data specialist. Right. So the opportunity's really, really great in this career, but you're right. It can feel like there is a lot of competition in this space because unfortunately, hear from people a lot of times, like I took this class and no one's giving me a job right away.

And so what, some of the factors that I see as an issue with. Companies are really nice people who not just have the education, but have the expensive. They need to know that, Hey right away, we're strapped for time because we don't have enough resources. We know that we can put you into this role and you'll automatically be able to succeed because you have the experience more than just education.

So for people out there who are in the catch 22 of like, well, I'm trying to get the education. Right. I'm trying to get the experience. That's why I'm applying to these jobs. What do you do? How do you solve that? So this is where building projects and building a portfolio works really well.

This is where volunteering, for organizations where you can use these skills can help build that progress. And then lastly, this is where those communication skills come in of sharing your work, right? Because as you're building out the project portfolio and you're sharing what your do mean, and your journey on mine, the right person is going to be able to be attracted to you.

So those are really the two tactics that I would take right now in this.

Resume Tips

Adel Nehme: I couldn't agree more. I love every single point. You mentioned one from, building a portfolio of projects. Sharing your work and even putting yourself out there and getting the experience and volunteering, so of course, when it comes to the practical side, as we mentioned here, breaking into data science, we need to talk about resumes, portfolio projects more deeply, and also sharing your work, building a community. So I'd love to first talk about kind of resume tips, right? How would you structure a resume for it?
 
Sadie St. Lawrence: Yeah.

I'm glad you're asking this question because just two weeks ago I was reviewing a couple of people's resumes and giving some feedback and it was like, I think I'm going to create a post from not bad to possess for a resume. Right? Cause that's usually what I see with resumes is it starts off. It's not bad.

But how do you make, how did we get you to really shine out? And so I think that there's a couple of key factors to remember the resume is not supposed to be a word dump of everything you've done and a linear journey through your career. The resume should tell a story and it should tell a story for the target market that you want to get it.

Does this mean that you should lie on your resume or put things in the hair? No, but what you want to do is you want to shake your resume and obey that highlights the key attributes that you have done for the job you're looking for. And so why is this important? So let's say you're going for a marketing data science role, right?

You want to make sure that when you're putting out your experience and your education, You're pulling out just the portions that really relate to that whole. Why? Because people who get resumes, how thousands of resumes to go through. And so you want to make it as simple as them. It's as simple for them as possible to be like, yes, this person has the right skills.

You don't want the person reviewing your resume to have to go through and try and dig and see oh, I saw a little bit here and a little bit. So one thing I would say is pick, have a really clear vision of the role that you are going after, right? Again, not just the data science role, focus on an industry or a business sector, and then craft your resume as a story. That's going to tell a story of why you're the perfect person for that role. the biggest thing I see is with the resume is people don't have a clear vision for what they're going after. They're just throwing all their skills out there, their experience out there and throwing it to the wind and hoping that something sticks.

So prior to drafting that resume, get really clear on what that was you want, and then pull out the portions of your experience and your education that apply to be able to tell that strong brands.

Adel Nehme: That's really great. So let's pick it out through an example, I want to be a data analyst in the healthcare space. I have a few experiences here and there. And healthcare bit touching data. I've learned a lot of data projects. I've done a portfolio of projects on healthcare data, How would you structure a resume for a data analysts going into healthcare for.
 
Sadie St. Lawrence: Yeah. So this one, because it's a technical role, you definitely want to have your technical skills at the top, right? So this is a goal where you're not going to be managing people. You're going to be an individual contributor. So you want to show right away. Here's my technical skills, right? So I'm a bullet point.

I know Python, I know SQL even putting in some of the libraries that you may have used and what you're familiar with. And then right away go into your experience, right? So on your experience side of things, you may not have worked in the healthcare space, but I bet you've worked on problems that are similar to what you would work on this healthcare role.

So what you want to do is pull out those problems and shape that story in a way that's going to apply here as well. And so That's going to be really helpful in terms of just making sure. Easier for the reviewer to read. Okay. Yeah. Maybe they worked in a consumer goods company before, but I can see how, how Ms.

Now applies to the analyst pool as well. And then finally, I usually end with the education side of things. And the education can go a couple of ways. people often ask should I put all the additional education I have on my resume? This depends for me in terms of whether you already have a bachelor's or master's degree, if you already have those things, you, the additional education you've done should come through in the skills that you have, right.

Not your bachelor's or master's. If you don't have the bachelor's or master's definitely add that on there, because I think it's going to show that, Hey, you've still done education, maybe in a different avenue and that's okay. But I think it's just important to know it's one or the other, but it doesn't have to be.

Creating a Portfolio Project

Adel Nehme: That's really great. moving on to the second element of breaking into data science here, which is like portfolio projects. What do you think are some of the most important aspects of creating a portfolio project? And what do you think makes a great portfolio?
 
Sadie St. Lawrence: I think the thing that makes a great portfolio project is the subject that you are interested in. So one of the best ones I saw was someone did an analysis. they were a big movie, but for me did analysis of all the movies that. Over the last five years. And they categorize them into all these really fun categories based on like how long the film is, who the director is.

How many were Marvel films, In total, just a really interesting and fun story. And they did it in a fun, interactive dashboard. what I loved about this portfolio project. You got to see their personality. And I think that's really important to remember too, is you're trying to break into a role is lecture personality.

See, because you're going to then find the right fit and culture, right. If you're really showing who you are and your personality is you're going to attract people where you're automatically going to Fitbit. So. One find a subject that you're really interested in and something that you're going to be passionate about when you're communicating those results.

And then secondly, find creative ways to tell that story. So you can definitely add it to a get hub page. You could create a medium blog post. All of those are great but maybe you go the extra mile. Maybe you make a fun little. That people can use to filter through the videos, right? Maybe it's an interactive dashboard.

I find creative ways to tell that story. And I think that's really what will make your portfolio?

projects team.

Adel Nehme: I love this, especially on the authenticity and having a great, genuine interest in the subject because there's kind of Nick saying who I interviewed as well on the podcast on async the data science interview mentions this as the halo effect, if you are genuinely interested in a topic, people will gravitate towards.

And they will be able to soak in that genuine and authenticity and that interest and that enthusiasm that you have for the podcast, which will translate for a much better interview experience.

Sadie St. Lawrence: Yeah, I couldn't agree more. I think softened times, if you're trying to break into the field, you can just feel like I just want my first chance. Right. And so you're willing to just do whatever to get that first job. But what I would say is don't lose, don't neglect that like, you really want to care about the culture of the team that you're going into.

And the only way to do that, it's to share who you are so that they can see if it's.

Adel Nehme: I completely agree. what do you think are key mistakes people make when creating a portfolio?
 
Sadie St. Lawrence: I would say doing what's already been done. So there's a lot of fun names out there. It's the, it's the, I think it's like a golden retrievers. To like aware Wolf right. In the golden retriever house. Like if I have this data set and then the like werewolf picture is like real word world data, right.

It's like a classic meme

Adel Nehme: Yeah.
 
Sadie St. Lawrence: Community. And it's so true. Like we all, like, this is why meetings are so great because we see it in automatically get it. But I think also more importantly, not just in terms of why this meeting is so great, but it's in terms of like the complexity of the two different data sets, but.

You know, we say like the Iris status said, it's so overused in terms of what people have done with it. So again, it can, when you tap into what you're really interested in, you'll find more interesting data sets, right? Maybe it'll use your net for this data. Maybe it heals data from your apple watch or your health tracker, right?

Like maybe you're really interested in RA and you start to analyze like NFT art purchases and what's trending in the art . Market. Right. Go into what you're interested in and stop doing what everybody else has done. Caitlin was a great place to find some free data sets and get started. And I think that's a great place to practice, but in your portfolio it really needs to be unique.

And so I would say the biggest problem or mistake that people do is just not make a unique portfolio.

Building a Data Community

Adel Nehme: So the last thing that we mentioned when we were talking about principles for, breaking out from the crowd, it's sharing your work, building community around you, I'd love to anchor this actually in your experience, launching women in data.

I had an amazing time preparing for this podcast, learning about your story while preparing for this podcast. And I find it to be a great testimony for the power of courage and community. So do you mind expanding on how you first launched a minimum data and kind of that story and how it led you to where you are

Sadie St. Lawrence: Yeah. So at the time I was working full time as a research analyst and I was also doing my master's degree full-time and obviously it was very busy doing both those things. Um, But I felt very lonely in this process. So I, I felt like I didn't have people I could truly connect with to discuss ideas, to collaborate with.

And it was really that need for belonging and connection that led me to start with the new data. And it really just started with my own personal need of community and then a broader vision for more equality in the space. So unfortunately in my master's program, you know, there was 30 people in our first cohort and there was only myself and one other remain in the program.

And so I really just felt the need to connect with other people like myself. And so women and data started with a meetup group and my local city. I thought that there was going to be a great attendance. Everybody was going to be excited about this thing happening. Unfortunately, as the time got closer, no one had showed up and I was feeling very discouraged and really just wanted to pack my bags and go home.

And thankfully, I decided to wait 15 more minutes after the start time. And one person came rushing in the door and she brought three other people on. So that was really the birth of living in data. And I think it also just goes to show like you don't meet that many people that were initially to connect with, right.

Like just finding one or two people. Is the start of something. And today?

you know, women and data's a community of over 30,000 people and 30 countries and 50 cities across the world. But it's really, truly incredible when you just put that call out there to say, Hey, let's connect, let's grow. Let's be how it may take time.

But eventually with some tenacity and dedication, it will grow.

Adel Nehme: So the last thing that we mentioned when we were talking about principles for, breaking out from the crowd, it's sharing your work, building community around you, I'd love to anchor this actually in your experience, launching women in data.

I had an amazing time preparing for this podcast, learning about your story while preparing for this podcast. And I find it to be a great testimony for the power of courage and community. So do you mind expanding on how you first launched a minimum data and kind of that story and how it led you to where you are.

Sadie St. Lawrence: Yeah. So at the time I was working full time as a research analyst and I was also doing my master's degree full-time and obviously it was very busy doing both those things. Um, But I felt very lonely in this process. So I, I felt like I didn't have people I could truly connect with to discuss ideas, to collaborate with.

And it was really that need for belonging and connection that led me to start with the new data. And it really just started with my own personal need of community and then a broader vision for more equality in the space. So unfortunately in my master's program, you know, there was 30 people in our first cohort and there was only myself and one other remain in the program.

And so I really just felt the need to connect with other people like myself. And so women and data started with a meetup group and my local city. I thought that there was going to be a great attendance. Everybody was going to be excited about this thing happening. Unfortunately, as the time got closer, no one had showed up and I was feeling very discouraged and really just wanted to pack my bags and go home.

And thankfully, I decided to wait 15 more minutes after the start time. And one person came rushing in the door and she brought three other people on. So that was really the birth of living in data. And I think it also just goes to show like you don't meet that many people that were initially to connect with, right.

Like just finding one or two people. Is the start of something. And today?

you know, women and data's a community of over 30,000 people and 30 countries and 50 cities across the world. But it's really, truly incredible when you just put that call out there to say, Hey, let's connect, let's grow. Let's be how it may take time.

But eventually with some tenacity and dedication, it will grow.

Adel Nehme: I'm really an awe about this story because the psychological barriers of getting over that discouragement and keeping on. is super impressive to me. And I've, what are some of the lessons that you can share when mustering the courage and the, 42 to keep forward and fostering a community of peers and mentors that can help you grow?

Sadie St. Lawrence: I really look at courage is a muscle, right? It's something that we have to practice and we have to strengthen it. And so I think we all need to strengthen our muscle courage so that one, we can put our true selves out in the world. We can let our ideas be heard. And so how do we get started doing that?

You start with small steps, right? You start by raising your hand and speaking in that maybe you start by volunteering to do that presentation. You start by taking those small little steps of courage and what happens. When you take that first little step and it wasn't as terrible as our mind leads us to believe of all the fears of that things that will happen.

We're able to relax and take a bigger job. And That's truly what has happened to means it was just a small step to say, Hey, I'm going to start this and see if anyone wants to show. And a few people. And so, that first step of strengthening that courage muscle is key, but then more importantly, I would say consistency and tenacity, very praisable here.

I think a lot of people are familiar with the hero's journey and it's this arc of highs and lows. And I think it's a really beautiful story and also very applicable to all of our lives and that okay, you strengthen your courage muscle and there may be a little high, but you must keep going on because there may be some lows in between that process as well.

And so. It's important to have that tenacity and to have that dedication discipline, and that only comes from having a vision of what you're looking to achieve. And so to be able to have that courage and to go through those hard times, it's really important not to have a vision of either your future self or a vision of what you're looking to create, because that will carry you on through those low moments.

I'm really an awe about this story because the psychological barriers of getting over that discouragement and keeping on. is super impressive to me. And I've, what are some of the lessons that you can share when mustering the courage and the, 42 to keep forward and fostering a community of peers and mentors that can help you grow?

Sadie St. Lawrence: I really look at courage is a muscle, right? It's something that we have to practice and we have to strengthen it. And so I think we all need to strengthen our muscle courage so that one, we can put our true selves out in the world. We can let our ideas be heard. And so how do we get started doing that?

You start with small steps, right? You start by raising your hand and speaking in that maybe you start by volunteering to do that presentation. You start by taking those small little steps of courage and what happens. When you take that first little step and it wasn't as terrible as our mind leads us to believe of all the fears of that things that will happen.

We're able to relax and take a bigger job. And That's truly what has happened to means it was just a small step to say, Hey, I'm going to start this and see if anyone wants to show. And a few people. And so, that first step of strengthening that courage muscle is key, but then more importantly, I would say consistency and tenacity, very praisable here.

I think a lot of people are familiar with the hero's journey and it's this arc of highs and lows. And I think it's a really beautiful story and also very applicable to all of our lives and that okay, you strengthen your courage muscle and there may be a little high, but you must keep going on because there may be some lows in between that process as well.

And so. It's important to have that tenacity and to have that dedication discipline, and that only comes from having a vision of what you're looking to achieve. And so to be able to have that courage and to go through those hard times, it's really important not to have a vision of either your future self or a vision of what you're looking to create, because that will carry you on through those low moments.

Adel Nehme: That's really great. I couldn't agree more. you're someone who is through women in data have had both mentors and have mentors, a lot of people, how should aspiring practitioners treat mentor mentee relationships. Make sure that it's very useful for the mentor, but they're also really benefiting from that really.

 

Sadie St. Lawrence: Yeah. So I would say the first thing is to look at the mentor as a relationship. And I'm so happy that you use that word because I think a lot of times, every well, everyone knows mentors are important and there's so many people who want to be able to find one. And so I like to give people some advice of actually having you first find a mentor, but that starts by just building relationships with people.

So how do you do that? You, you do that through conversations, through finding common right, and creating connections. Most, all of my mentors have been very organic, started by building a relationship with them, having that commonality, that common connection. And then as that relationship grows, a lot of times you just naturally enter into a mentorship, but halfway through you go, are you my mentor?

And they go, aren't you my mentee. And, and it happens very organically. Right? That's the best case scenario, right? It's where those connections happen organically. And so I tell people, stop focusing so much on finding a mentor, but more on building relationships with people that you really admire. And I think if you have that mindset, it takes a little bit of the pressure off of it.

And then when you get into that mentorship, some of the things that. You can do, I've heard people say, Hey, you need to be of use to your mentor. Maybe help them out. Or. And that's good. I think if there's an opportunity that presents itself, we definitely should. But for me, why I mentor people it's because nothing makes me happier than seeing them grow and seeing the change.

And so the best thing that you can do for your mentor is to work on yourself. Because when they see that the time and energy and the advice that they've given to you is making a difference. They are going to be so happy and they're going to want to pour more back into you. And By working on yourself at seven show up to your meetings on time.

Do the things they ask and the homework come in with questions and be prepared. There are simple things, but it will show up for their mentor and they will be happy to give you more. Once they see that it is paying off and they want nothing more than to see you succeed.

Adel Nehme: That's really great. I love that. And especially at the end, when you mentioned like doing the homework, I think nothing makes a mentor more happy than seeing that their advice is being actioned and that's what makes it worth it for the mentor themselves. Given that also your work as a community organizer and that you've, put yourself out there. Whether in women and data, or on social networks, how do you . Approach the imposter syndrome? A junior practitioner may have, right. When sharing their work

 

Sadie St. Lawrence: Yeah. So I would like to clarify for people that the poster syndrome never goes away, it just changes. Right. So I'm not here to discourage anyone, right. To be like, oh, I'm just trying to break into the field. And I have imposter syndrome. Oh, don't worry. You'll still have it as to still move up in your career and lead.

You may even have . More of it. Cause there's more responsibility on your shoulders. So how do you make friends with your imposter syndrome? That's what I like to do of like, how do I look at that? And really not use that to limit me, but use it as a way to build my courage muscle. And so I think imposter syndrome can be a great thing because it brings up for us where our fears are and where we need to work on our courage to dive through.

So if you have a fear of sharing your work online, start with small things, steps, Start with having to go for yourself to maybe just post once a week. I know people who, when they started posting to. It was so scary for them that they said, Hey, I'm going to post. And then I'm not even going to look at any of the results.

And maybe they'll tell, you have to start. don't check back every 10 minutes. See, did somebody like, did somebody comment? that's a good starting point of just put it out there and then as you start to do that, right, you'll realize, oh, It's not as scary. There aren't as many trolls out in the world as we think that there are right.

And actually people, actually, people are rather counting and supportive. And so once you start to get over those first barriers and you'll be able to do it more. So my advice is use your imposter syndrome to see where this change in your courage set small goals for yourself. And stick to that consistency.

And eventually, they'll be able to break through that there.

Adel Nehme: Yeah, I couldn't agree more. Definitely imposter syndrome doesn't go away. Frame it as being friends with your imposter syndrome and using it as a tool to push you forward that's something that I find a struggle with as well. You know, I host the podcast here, and imposter syndrome is still something that I struggle with.

Given your experience as a community organizer as well. Someone who's worked on can increasing diversity and equity and data science. I love to understand from you if I'm an applicant rates and I'm from a minority group and I'm applying for a job and I'm interviewing with a company, how do I understand what are questions I need to ask to understand if this is the type of organization that will lift me up, or I will have to fight much harder than, male counterparts, for example, to be seen.

Sadie St. Lawrence: It's less about the questions and how you feel in the situation. And why do I say this? Because. I haven't met a company who's going to come out and straight up say we don't support the diversity. Right. And we're not inclusive. Right. No one will ever answer that question that way. And thankfully, but what happens is sometimes they may say, yes, we supported him and you do all those things, but their actions are different than their words.

Right. And that's a very discouraging thing. and something that we want to remain. So how do you get away from that? You really look at their actions. And how you feel based on how they're treating you in the interview. So I tell everyone this going into interviews, they're not just interviewing you, you're interviewing, how did they respond to your answers? Do they respond in a collaborative way and say, yes. Did you think of this or is it in a closed, aggressive way that doesn't make you feel good? Right. And feel free to take the insight you're getting back from now. Not as you did something wrong, but insight into what is the culture of this organization.

So I would say less of like asking questions and more of being aware in the interview to those small, subtle. body language and tonal things that will give you insight into what that overall culture.

Adel Nehme: I couldn't agree more like culture is such an important aspect of being able to succeed within the organization. And that will, regardless of your skill. Like you have to fight twice as hard to get those skills out there. That's an uphill battle that I don't advise anyone to want to have.

And that's why I'd love to, I love your perspective here on being able to measure of the company's throughout the interview process, to .Be able to make that.

So now Sadie, before we wrap up, I'd be remiss not to talk about future trends that, really shape the future of the industry and how we think about data jobs a day. So what do you think are some of the trends aspiring and current practitioners should be on the lookout for, as they grow in their careers?

Sadie St. Lawrence: Oh, I'm so glad you asked this question because I do love to talk about the future and most of the time I'd rather be in the future than here, but it's important to be involved in places that want to, so yeah, I think there's a couple of key things. I think. One of the things that I'm most interested in is how blockchain technology is going to change data careers.

So at the core of what blockchain technology is, is a database, right? It's transaction and record. What makes it so special is that it's decentralized. And from the decentralization, we can reach this consensus. And so there's a lot of great things happening in this space and the applications of this through now, web.

And this will change a lot of how businesses operate. And it's really important for data professionals to be aware of this because how businesses operate and change is one where you get the data from what those streams of business operations are that you're looking at. And so I think it's important for data professionals to not keep their head in the stand with.

machine learning models and data visualization, but to look a little bit further out at the broader industry. And so I would take a keen look into web three and into blockchain technology. And as a practitioner in this space, I would be someone who would be encouraging the use of this at my organization, because one of the most beautiful things about blockchain technology is.

It is timestamped and fair height. So what happens to this data and it's very clued data, and nothing makes a data scientist sitting more than having very clean and accurate data where it's viewable, right? No, what that record was what happened. So if I was a data scientist, I would be wanting to have my organization use this technology.

That's going to make the work. I do a lot easier in terms of the cleanliness of the data that I'm able to work.

Adel Nehme: That's really awesome. And harping on a practical side, if I'm a practice, practicing data scientists now, and I want to learn some techniques or much more aware of blockchain technology and web three. What are technical skills? I should know.

 

Sadie St. Lawrence: Yeah. So I first start, before you go into the technical skills is start with just an awareness of where the industry is at today. So there's a lot of great, webinars happening, women and data right now is doing a whole series on web three, the applications and what this means to data professionals.

But I'd start just kind of with a broad awareness of just getting your head around this technology and the applications of it. From there, which you're going to want to do is similar to data science, where you want to pick a language of like, are you starting with Python? Are you starting with all our don't do both at the same time, like just stick to one and get good at one is you're going to want to find a chain that you want to use.

So blockchain is one chain, but there are actually hundreds of chains out there. There. hi Dara hash pack or hash graph, which is a chain. There's lots of different chains that you can work with. So it's similar to data sides and that space of like, don't try and do it all at once.

Just pick one and understand how a smart contract works, how a token works. And then from there, you know.

you can kind of go where.

Final Words

Adel Nehme: Finally, Sadie, as we close out our episode, do you have any final words before we wrap up?

 

Sadie St. Lawrence: Yeah, I would think, I would just say to all the listeners is stay curious and don't be afraid. To start with a blank page, a blank notebook, a blank canvas, start with something new and create the new yourself to let your true self be seen, because that's really how you're going to find a career that brings you the most joy.

 

Adel Nehme: That's really awesome. Thank you so much, Sandy, for coming on.

 

Sadie St. Lawrence: My pleasure. Hope to talk against it.

← Back to Blogs