How We Built an Inclusive Data Organization: Careers, Community & 50% Women

Speaker 1 [00:00]

Thank you very much. I'm Xia. I'm head of the data and cloud at Engovay. April 2020, the start of the pandemic, and kindergarten was closed. Two small kids under six at home. And I, me at the kitchen table, writing my master thesis and finishing the last semester of my master degree. I was about to enter the data job market for the first time without any data experience and years of raising children. I started applying, and after 70 applications into silence, I stopped asking me the wrong questions. the question wasn't about am I good enough for them the question was about does anyone's hiring process be designed to find some funded talent like mine so I would ask you the same questions would your data team recognize talent like mine Let's look at the data. We are data people, so the data will not lie. According to the WomenHack report this year, only 26.7% of the technical workforce is women globally. And what surprised me most is 50% of women who managed to enter the door into the tech but leave the tech at age 35. And not because of them lack ambition, not because then they cannot do the work. Because of this, 72% of women in tech experience broad culture. So the problem is not just about letting people in, it's about the environment. And the challenge is to support and retain the talents once they are in the field. Because I know it, I experienced it, luckily I found my first job, but over the four years I changed jobs four times. I'm looking for a place where I can learn, grow and contribute in a supported environment. Yeah, it was four times, four different teams, four start, fresh start. Every time I thought, this time will be different. But each time I left disappointed and exhausted. And so luckily in 2024, I found Engovay. Don't ask me what ANOVA stands for if you're already in the lightning talk the first day. If you don't, we'll watch the recording. But I can tell you, ANOVA, we are about 200 people and about 30 people who work in data and cloud field. And I won't tell you ANOVA is perfect. No, it is not. But it's different. and I started as a data engineer there and I was promoted to the head of data and cloud one year later. The talent is the same, still me, the same person but with a supporting environment the person can demonstrate fully their potential That's what I learned. So the motivation I standing here is that we want more NOV, more supportive organization, so that you maybe also take some ideas from this talk. I brought four levels with me what we do differently at NOV, so So they are in hiring process, in the career, we have the internal community, and diversity charter. I will go through each of them. The first one, when people about the door, which people we want to let them in. three practice I brought actually as easy as the first like job descriptions and the interviews and diversity panels and so actually it's not that complicated at all we start with the job advertisement it's and the words matters a lot than you think. In technical job posting, we experience a lot of masculine coded words like dominate, competitive, ninja. And researchers find out such kind of words will discourage women to apply because they don't feel they belong to this group, so they won't even read the requirements of the job description. What you can do is very simple, just use gender-neutral words using commoner phrasing, and the result is 42% of more women applications in your your talent pool that's easy and if you want if you just hire two male colleagues and the next you want to actually more woman coming you can even go a step further you can add words like support empathy collaboration we have the face like empathy and corporations as important as the technique skills in our job advertisement so that's how we do differently and then it's the structure interviews if you do the blind in review that's that's great and also the same questions objective scoring that's also crucial and also from the women head and they report 46% more female candidates in final rounds using these techniques. And if you are not able to do the blind review, no matter what reason, but you can do also something to recognize the unconscious bias. What is unconscious bias? It refers to automatic mental shortcuts our brain thinks about a person, a situation and information without aware of it. For example, familiarity bias. This person comes from the same university like the other colleagues, so he must also be good at the work. Or the affinity bias. This person reminds of VXX. Yeah, he is welcome to the team. Or the halo effect or horn effect, which has a positive or negative bias. If you see a little small mistake, maybe someone is a bit late for the interview, you just say okay this person cannot do anything that is also bias and I will not go through all the bias but this unconscious bias training are very crucial if someone in the hiring process and of course you see this this is very it's not real we report as a lot of you reported when going to the technique interviews we also the mayor interviewers and of course for the female candidate we we lack of different perspectives so in our case we yeah we have we always try to ensure that at least one different perspective involved in the panel. And then the second is about the career. I would like to talk about the non-linear career and also life-phase flexibility. I did a survey in our company preparing the presentation. I was also surprised to find as high as 59% of people who are coming from who work for data and cloud have such kind background they are civil engineers from philosophy they started and also language and HR I was so amazed because I know they were the great engineers and analysts so we cannot only see what the people in the past and decide what they can do in the future And let's also confirm from the report from the work change report from Blinking last year, AI is coming to work. So two more and we will have two more jobs and then compared to 15 years and 70% of the skills and in 2030 will be different we're totally new so what we are looking the talents the ability to learn and the problem to solve not the not only about what they achieved in the past and it's not real that in tech we feel very stressed the AI new model every day and we have to keep pace otherwise we are outdated and and it's burn out and also the new release deadline is we experience every day that's why we have to take care of ourselves and in at in at our in an away so part-time and career break and re-entering are not a box and me myself and in the leader position I also do part-time I do it intentionally and it was good if you do you you have the trust and good structure it works so we have I think that's that's the important key you have to take care yourself first and the organization should support you and third level is what I passion about and the community we are here in a big community pi com the PI data but we have internal community because not everyone like me very active go out after work outside and some people have their our private life and we cannot say hey go to this event meet her no but we instead we create our internal community data science community it's called and we do exactly what we do here we have pictures we meet each other regularly, we have lunch together, we do hackathons, we organize workshops ourselves and to teach other advanced Python. Last year for example we have certification sprints like AWS, my German, AWS sprints that we have 20 people who learn and and celebrate it together and exactly what we're doing but we do it at work time and and it is worth it even though we need to read yeah and it's why it's worth it because at one side we embracing the data mash architect that means the data people are in the domain team so data engineer data analyst so in small teams are only one or two data people in team and some to this community then keep there to bring the people together so that they don't feel alone they can still learn from engineer from cloud staff and this is important for the company to break down the silos and to share the knowledge and so also to learn to have a belonging a safe space for everyone and so for my in my opinion it's not nice to have but it's a strategy a clever strategy to keep the talent and to encourage them to contribute to and to grow that is also actually one story that is also where my mindset change from individual contributor to the leadership position it's a really thankful to this community because I try to realize and have I make more impact helping more people than just just to fix it back in India so So this is really nice stuff. And lastly, the level four is diversity chart. Actually, each European country, no matter if you're from Portugal or from Italy, each country has a diversity charter. Have you anyone heard about it? Please raise your hand. No one? Oh, one. That's great. Maybe, yeah, that's great that you can bring with you. This is a written commitment, actually signed by our CEO, and in Germany it's defined in these seven dimensions, it's about age, migration history, social background, and so on. And it's a commitment that's signed by the leadership, and it's a signal to all the employers that diversity is not oral, it's not just written in the job description, but we really want to achieve it. Actually, it's not from the leadership. Actually, this is an initiative from our diversity working group. So I was in this group, so it's actually the colleagues, we just form a group and we promote this and we try to do the girls' day and to get budget for the diversity training. So all done by ourselves, not from the leadership and also to some training for, also to organize the trainings for them. So you can take this with you. And the result is we achieve 50% plus women in data and cloud. Actually, it's not only about the number here. It's about how it feels working at this place. When we're sitting in a technical, discussing SysOps security, Terraform, you see the more of the half are women. And every time, such good cooperation. And this is also confirmed from the partners who work with us. So the working with you is so different. Working with NOVA is so different. So that's why we want to continue and keep it. So we come to the conclusion, what is the one thing you will change next week? You might say, I'm not hiring, I'm not the manager, I'm not a CEO, I'm just a junior data scientist or junior data analyst. My answer is actually everyone in this room can start. You can start where you have control. If you are a manager, good, review your job posts, descriptions, try to review the structured interviews and if you are not, no worries, you can also make difference as I said the diversity working group are just normal in normal colleagues they're not there's no leaders these involved you can make a difference and also what directly is to maybe support today or tomorrow your colleagues and share what you you learned that is already a support start So in 2020 I sent a lot of CVs into void. I didn't know that someone was building a room for me and now I'm trying to build this room for someone like me in 2020 and I really want you can join me to build more inclusive and and supportive environment for diverse talents. And last but not least, I have to thank two women who recognized me without them. I was not standing here. They supported me. They recognized me. And now I'm building this together with them. With that, that's all my talk today. And thank you very much. If you want to connect with me, yeah, this is my LinkedIn. And happy to answer any question if you have.

Speaker 2 [20:22]

All right, thank you so much for your talk and we have some questions for you. Yes So let's start the first one Can you recommend resources how to have a successful transformation process of the workspace culture of a pro like? software team into a more inclusive environment Like can you recommend resource resources to have a successful transformation like

Speaker 1 [20:52]

As a result, I myself did a lot of online courses. I was alone. In this case, I would suggest to find mentors that will share her experience. Because I was, in this transition, quite alone and have to try, made a lot of efforts myself. So that's why, actually, I offer the mentorship to women Since two years I want to give back because a lot of things I think they can learn much faster to avoid more effort. So if you have, you can just contact me, write me, I will share my knowledge with you.

Speaker 2 [21:40]

Thank you. Everyone is talking about AI. Do you think it is a good idea to use AI tools to help in creating a more diverse workforce, or is AI reproducing bias and the masculine view onto job descriptions, etc.?

Speaker 1 [21:57]

That's a good question. We don't have the AI in our hiring process, but I know in the big companies they do have. So if your company does have, please check the model to make it not biased. It's really important.

Speaker 2 [22:23]

Yeah. So next is, do you think that compared to bigger companies, startups provide a more inclusive environment? And if one of your previous jobs was in a startup, did you notice a difference there?

Speaker 1 [22:42]

kind of startup. I have worked in big corporates and small consultants. It's not about the growth, it's about the team, really, the people. So it's hard to say. So I think it's important to start from the small team the diversity the mentorship you can make a change there and maybe startup can also more stressful as as it is you know it's a more less people and do more work and can be challenger but in some life phase it's it's good and for the growth but it depends on also on the life phases what what is your goal is.

Speaker 2 [23:34]

Okay, so then what recommendations do you have for supporting women in a very male-dominated field where they are constantly working against pre-built opinions?

Speaker 1 [23:48]

Okay. I think that's why we need more women on the stage to, like, so that give our talk, give our opinions and different voices. That's why I decided to try the public learning on LinkedIn to share what I learned, what works, what doesn't work, and also try to be visibility for other women. So I encourage you also doing that.

Speaker 2 [24:30]

Are the community events you referred to designed as a safe space? For example, Flinders only.

Speaker 1 [24:43]

You mean the community, the external community?

Speaker 2 [24:46]

The community events you referred to, I guess like the internal ones. How are they designed as a safe space?

Speaker 1 [24:54]

what I recommend is to build an internal community in your company. I don't know how big your company is, but as I said, you can start a small group, a learning group, but external communities, there are a lot of PyLadies, you know, and PyData. But for me, I think this is a game changer for me because I work for small, big companies. Sometimes I work alone for project even in big companies I was sent to alone to a project or because you know the budget and and that's why the internal community make a difference that bring people together and to share and learn together we have we just finished the telephone spring so also people also cheer with them share their success they did it so maybe you can you can also try in your company and let's start with your colleagues yeah

Speaker 2 [25:59]

Yeah, maybe also like how do you make it a safe space or like like something that you do like to make it a safe Space like maybe you could give an example for that, too

Speaker 1 [26:13]

how to say it, no problem to make a mistake, not afraid to make a mistake, you can share anything what you want and this is a space that anyone can say opinion and support, try to be supportive, that's how we do it.

Speaker 2 [26:38]

How is Girls' Day contributing to a more inclusive organization?

Speaker 1 [26:45]

Girls' Day. Yeah. Okay. We also, yeah, we also organized Girls' Day this year with also connection with the data science. So yeah, we try to show the young girls, not young girls, 13 years, from 13 years that they start to have a view of what looks like, what it means data science, what means data engineer, so that they can start early to think about that influencing their choice of studies in the future and the career then maybe we have more women in the STEM.

Speaker 2 [27:22]

Okay, when you talk about data science communities, do you think of them as especially diversity-focused groups, or is it more a mentoring and fostering idea?

Speaker 1 [27:37]

Both, because we also, mentoring, we have actually very active, different, we have special diversity working group, and also women group separately, they're doing also mentorship. But in the data science community, maybe more focused on knowledge exchange, on the growth, and so that they can learn from engineers, from analysts, so different. Yeah, maybe less, yeah, I would say not the focus of diversity, but still it helps people to at the place that they can show their ideas.

Speaker 2 [28:29]

Okay, we have two more questions. I think we have also time for that. So, Nan, do you know of good examples of inclusive job descriptions? Maybe like to look at.

Speaker 1 [28:41]

As I said, the good or very good is gender-neutral and also include the phrases like encourage under- presented groups to apply, also to add if you are also one thing is also important that think about the list of the requirements because the exhaustive list of skills also threaten the under group under present groups to apply and also write in the description to encourage them to apply also when they are not 100% satisfied with the requirements.

Speaker 2 [29:25]

And then I'll find a question. What is your understanding of bro culture?

Speaker 1 [29:32]

Actually, my experience, unfortunately, was also, actually in my internship, the first job, it's really a personal story, and the senior developer who hired me gave up me after the first month. He said, your Python is so bad.