Come as you are: Transitioning from Science to Data Science

Speaker 1 [00:03]

Yeah, thank you very much, Jessica, for your nice introduction. Yeah, my name is Hanna. I'm a former scientist, a neuroscience PhD, and I'm now working as a data scientist and software developer at a small company called IntBlick.io. Now that I'm a data scientist, I often get contacted by former colleagues and neuroscientists all around the world who asked me, I want to transition, how did you do that? So this talk is for you, it's for all the academics who are thinking about leaving. I would like to outline my personal journey and also as a bonus I included everything I wish I had known when I was searching for my first job in industry. So if you scan this QR code, you will go to my website, and I prepared a little website with all resources that I will mention during my talk, and yeah, go there, there's lots of stuff that you might find helpful. And I will show the link on my last slide again if you miss it now. Before I introduce myself and tell you why I left academia. I would like to know who is currently here in academia and thinking about leaving? Oh, a lot of people. And who is in industry but has left already? Oh, also like half. And is there anybody in industry like currently hiring people from academia. Oh, yeah, nice. So, go to them, talk to them after the talk. One more. Yeah. Great. Yeah, this is me. I'm working as a data scientist at InBlick.io. We built end-to-end data solutions with a focus on geospatial data from front end to back end, like everything, web platforms, apps, dashboards. I was before, until 2020, I was in academia for nearly 10 years. This is a picture I took of my head and my brain during my last postdoc position when a participant didn't show up in the MRI scanner, so we had some free time and could play around. During my PhD, I specialized in analyzing functional magnetic resonance imaging data, like fMRI. And then during my postdoc, I was also in a lab at Universität Potsdam where I was analyzing brain data mainly and in a brain research group. And I really loved my my job. I really loved doing research. Collecting data in an MRI was like flying a rocket. It was really great. Also, making up questions, which you can solve with data, and writing journal papers, and really diving into the topics. But I think always the best part was when I could walk out of the MRI machine with my hard drive, with all the brain data, and go to my office and analyse it, which was the best part. But when I was offered an extension of my contract, it was a fixed-term contract like most contracts in academia, I still had with mixed feelings, because it was actually my 13th fixed-term contract in academia. I had to count it because my German pension insurance asked me to recently. If you're in academia, you will know the act of fixed-term employment in academia in Germany. It basically means that after six years, after you finish your PhD, you have to get a permanent position because then it's very difficult to get extensions for your contracts. And then I was like, okay, it's going to be more difficult, and the only... What can I do? I have to become a professor, but becoming a professor is very competitive, and for me it was like, okay, being a professor is more going to meetings, teaching, and what I like most is analyze data and do research, and this is not something that you normally do as a professor. It's like a different career, a different job. And when I was thinking about this, then I stumbled across an opportunity on the Potsdam postdoc graduate school website. They offered career coaching by career coach to pay for that, and I went there, and I think it was meant to go there to enhance the academic career but after the first appointment I already knew that I wanted to leave academia for good. So then I was there and I was like okay. I read this on Twitter recently by a doctor female former academic she writes we've never been told that the academic career path is only one of the many possibilities from PhD to prof that's what we've been told but when you are out suddenly there are myriads of possibilities yet you're not trained to pick the path you want and i think this is some something like many people know from academia it's it's like we we are working in a bubble and there's not not so many connections to industry at least in my field i think in it that's a bit different but in neuroscience it's like that that. So, where would I start? So, I was really overwhelmed at the beginning. So, I would say, if you ask me now, start like this. I would ask yourself three simple, but I think also very difficult questions. And they are, what do you want? What do you bring? And what do you need to learn? And to find out, I think it's difficult because at the beginning it feels like everybody is asking you what cake do you want to bake and you don't have a recipe book and you don't have any idea about the stocks you have available. So I would start like the what do you want question. I would start with reading job ads. They are a bit like a recipe book and then talk to people outside academia as much as you can and I would recommend to start applying as soon as possible and don't wait for your dream dream job but just start applying because for me it was a very good opportunity to to get acquainted with the job market and to talk to people outside academia, to talk to data scientists and to people who hire data scientists. They can tell you a lot about the job and the everyday life. And for me, it was really helpful. I will go through the questions now one by one and tell you a little bit more about it. So what do you want? I I think the first thing which I find really important is to make a decision whether you want to stay or whether you want to leave. Because I think if you want to stay, you have to put all your effort in making a career in academia and you have to be decided. And I think if you really want to become a professor, you definitely become a professor, you can do that. But if you're undecided, I think you should also make the decision to leave as soon as possible, which doesn't mean that you necessarily have to quit your job immediately, but just shift your focus from writing the next peer-reviewed journal paper at the first author to building a network outside academia. And then I would recommend to get as specific as you can be. what you want. And at the beginning, it will be difficult, but ask, like, your motivation. If you're here at PyCon, you will probably already be programming and love programming, so that's a perfect start. But even as a Python developer, you still have myriads of possibilities. And then, yeah, ask yourself what you like and what makes you forget time, what you like to programme. And then also, I would recommend to be also specific with your boundaries. Because for me, for example, I am located in Berlin, I'm based here, and I don't want to relocate for my job. So, at the beginning, I was like, can I really share that? Will it lower my job chances? And now, in retrospective, I think it is really important to share your boundaries because it will make it easier for you to find your dream job or the job that you like, and it will also make it easier for the company to find somebody who is a match and who is really the one they want to hire. So, don't try to please everyone. And then the next question, what do you bring? It's also closely connected, I think. At the beginning, also, the data roles will be really confusing. For me, they were confusing. And there's a very nice webinar by AI Guild, which you find on YouTube and on my website, which gives a very good overview over the definitions and the skill set that is required for these data roles. But always keep in mind that the different companies, they have different interpretations of the data roles. And it also differs if the company is small or big. So in smaller companies, you are more often doing everything. And it's less specified in bigger companies. And then if you have an idea which role you are interested in, then you can look at your skill set and think about what do they already bring. And in my experience, academics tend to underestimate the abilities they already have. So you will be probably programming. You will be trained in analytical thinking. You know how to solve problems. problems, you know how to work independently, you might have even been working with data, so write everything and highlight it in your CV. Yeah, and then adjust your CV. I think this is a very big topic which I could give another talk about, because CVs in industry and academia are really different. I would like to show you just the three top differences for me. This is my CV that I used to apply for my last postdoc position. I put many of contracts i had and i started with the education part um on top um yeah i put my publication in it's a very long cv so and this is the one i use now um as you can see the professional experience is one of the the most important parts and also the technical skills websites. And if you are applying for a job, highlight everything that might be interesting for them, and try to, like, adapt to the communication style, like, of the companies. And if you have a GitHub profile, include the GitHub profile in your, yeah, and make it short, not longer than one page. But yeah, talk to people outside academia and get help, because I think at the beginning it's very difficult to know how to do it. Okay, and then what to learn. I think this, yeah, it definitely depends on what you bring and what you want to do. So you might have a data role in mind that really interests you a lot. And then if you see your skill set, maybe you want to become a data engineer, you see that you're missing some knowledge on cloud infrastructure, and you decide to learn that, then I would really recommend to document everything on your GitHub. If you have a GitHub in industry, very important. It's a bit like a business card or portfolio. It's a big bonus if you have one and if you can show something. Make a project that you always wanted to do or use the tech stack that you really want to show and include a nice README so people actually immediately see what you're doing and why you were doing it and it will be really helpful. And then a career in tech is less about learning a specific tool as it is the willingness to learn a different tool when the time comes. Don't think that you have to know everything when you move into industry, because you will have to learn different tools anyway. You will probably have enough just to start, and then get going, because that's what everybody is doing. Oh yeah, and then my favourite part is, become part of your new community. I would really say go and attend meet-ups and get involved with the community, what you want to get into. So when I left my job, actually it was January 2020, so one month before the pandemic and i already was involved in my in the community and i was so happy that i was like there were like people that i already knew and um they were like noah and kat they offered me to an online boot camp which they did like for free and um it was really helpful for me it's also very helpful to find travel companions or other people who were looking for a job which you can peer program with or do a project together and um encourage each other and then consider contributing to open source um i think this is like a really great way to give something back to the community and to like we we have to to um improve the tools that we use and it's great if if you would contribute to that. Maren here, she is doing sprints, PyLadies sprints for underrepresented groups at PyLadies, and also Data Umbrella is doing sprints online. Yeah, if you saw the talk by Rashma yesterday, and it will be on YouTube, you will find lots of resources to how to get started. And then get involved in Twitter. I personally love Twitter. There's also a big community of former academics. And there's one Slack channel founded by Dr. Nicole Betts. And it's called Moving On From Academia. I also included a link on my website. It's a nice community on Slack with people who are leaving not only to tech jobs, but anywhere from academia. And then talk to people as much as you can about your job search. And because also the hidden job market, it's like I think 85% of the jobs are not on, and they don't even have an ad. So it's just word to mouth. And this is actually how I found my job as well. It was like on AI Guild Slack channel. My boss, he promoted a boot camp, actually, for academics. And I called him and told him, I don't need a boot camp. I just want to work for you. And yeah, he hired me on the spot. I didn't even do an interview. And not a technical challenge. Yeah, and it's a good opportunity to talk about your salaries. If you ask nicely, you will find people who will tell you what they earn and it's a very good start because in an interview you will be asked about your salary expectations and it's good to have an idea what people earn in the industry or in the job that you want to do. And then finally I would recommend to get in referrals because I didn't know anything about that when I was leaving academia. Referrals mean that if you are working in a company in tech, it's often that if you refer somebody and he is actually hired, that you get a bonus. And it can be like just a bottle of champagne, but it can also be like several thousand euro. So if you ask for a referral, this is like a win-win situation, possibly. So you get a job and they get a bonus. So, yeah, ask for referrals. Yeah, and the year is 2032. 97% of former academics are now employed as a data scientist. Well, we will see in 10 years, maybe. I am like really happy now as a data scientist I'm like really enjoy my my job I can do what I always wanted to do like analyze data and I can like like now I have I have a permanent contract and I can like really dive into it and focus on like enjoying my my work and getting better what of what I actually do. So, I totally recommend it. Five stars. And, yeah, if you, yeah, do it. Become a data analyst, data scientist, data engineer. Yeah, and I would like to thank all the great communities worldwide and in Berlin. And this is my company, INNBLICK, and also especially the Pi Ladies and Women in Machine Learning and Data Science and all the people who make it happen. I'm really, really grateful. And especially to these communities, I feel like really connected. Thank you.

Speaker 2 [21:41]

And I think I'd say on behalf of everyone, thank you, Hannah.

Speaker 1 [21:44]

Thank you.

Speaker 2 [21:44]

Thank you. what a great talk and I didn't come from academia but I resonated a lot with things that you were saying so I really appreciated it I'm just getting the questions up now if you have questions please pop them into Slido and you can also vote on them to let me know which ones you want me to go with first so here we go once at your first non-academic job what should you do first

Speaker 1 [22:18]

oh that's an interesting question yeah I think it's like in every job you I think you should listen first what you're like co-workers tell you and yeah try to get connected to the work that you actually going to do

Speaker 2 [22:42]

Yeah, that's a great tip.

Speaker 1 [22:44]

Is it good?

Speaker 2 [22:44]

It's good to know who to ask the question

Speaker 1 [22:46]

ask the questions too yeah also maybe you can add something to that if

Speaker 2 [22:50]

Yeah, I think this is a good answer of like, you know, find out who is in charge of which domain and who can give you the answers you need so that you know who you need to go speak to when you're given something and you need to find out more.

Speaker 1 [23:02]

out more. Yeah, yeah. That's a pretty good tip.

Speaker 2 [23:06]

Do applications with referrals have higher priority to get a job call from a company in your experience?

Speaker 1 [23:14]

I don't know anything about internally, but I think it's a very good opportunity for the company because if you're referring somebody from your network, they are more likely to have a similar qualification. And I think it is also very difficult for the companies at the moment to find skilled people. and it's just also, they would just be happy to have people applying.

Speaker 2 [23:46]

yeah yeah I agree I think it does help also yeah in my experience um especially potentially like if your background is slightly different to the role that you're applying for I think having a referral actually gives you a great advantage so I think your tips there were spot on actually I wish I had known that yeah me too

Speaker 1 [24:08]

Yeah, me too.

Speaker 2 [24:09]

um okay here's a very popular question um do you think that age is a factor in the industry for employment

Speaker 1 [24:19]

I think it's everywhere, no? Yeah, that's why I would also recommend to leave as early as possible. If you already know that... ...good or bad about it, it's just yours.

Speaker 2 [24:39]

Is there anything you missed from academia then?

Speaker 1 [24:42]

What I miss? Yeah.

Speaker 2 [24:43]

what what do you miss

Speaker 1 [24:47]

Sometimes it was just working without having a real plan to explore and have time to explore. And you try a tool and it doesn't work, you take another one. But it's not something I really miss, it's just something I also did and it was fun.

Speaker 2 [25:14]

yeah fair enough I'm okay so someone is asking what was the salary that you asked for I don't know if that's a very appropriate question but maybe in a more abstract form do you have any regrets regarding that so like I think what you highlighted was understanding what you're worth which is really important to know what you're worth and did you feel like there was any negatives for you in that experience of navigating negotiating and understanding your worth um maybe as a as a career changer in that sense yeah

Speaker 1 [25:56]

Yeah, I think it is difficult, especially as a career changer, but yeah, but there's like still room for improvement and that's OK.

Speaker 2 [26:07]

there's also a lot of good websites that give you estimates around what different roles get paid um and yeah if you come to meetups and talk to people they are normally one-on-one it's okay we don't mind to share like i think it's really important to normalize sharing about salary um probably would also not feel that comfortable saying it on the recording

Speaker 1 [26:28]

No, not on YouTube.

Speaker 2 [26:30]

Good one for trying How could companies attract academics more efficiently do you think

Speaker 1 [26:42]

Yeah, I think it's what I had the feeling that the companies, they didn't really know what they could expect from academics. And I think if you ask the people already working maybe in your company, there will be lots of academics and maybe get them on board for the hiring procedure because yeah I think it's like one of the biggest obstacles is that we don't like really have like good understanding of like what industry does and what academia does and we lack this connection and if there would be a better connection, it would be really helpful.

Speaker 2 [27:33]

Yeah, great point. I think we're also, at the place where I'm working, we're also actually working with the PhD and the TU universities here in Berlin, and I've really noticed, like, I don't have an academic background at all, but I've really noticed, like, the different ways of working and the different understanding. so I think yeah a company that has someone like yourself who's already made that transition actually is probably in a really good position to attract more academics yes because you understand yeah how to onboard people how to get the most work to their strengths fantastic thank you so much Hannah and for such an interesting talk and yeah please join me in a round of applause thank you