How to Become a Data Scientist, Data Analyst or AI/ML Engineer: Are Emerging Technologies for Women?

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How to Become a Data Scientist, Data Analyst or AI/ML Engineer: Are Emerging Technologies for Women?

How to Become a Data Scientist, Data Analyst or AI/ML Engineer: Are Emerging Technologies for Women? – There are many opportunities for women in emerging technologies, almost all the roles that you can think of. Be it a data scientist or data analyst, ML scientist, ML engineers. All these roles are open for women and the field is very encouraging towards women. Hi Guys, so this women’s day we have a very special video where we’re gonna talk about Are the emerging technologies for women? So the emerging technologies we mean AIML, Data Science and Data Analytics and to discuss this we have some great Springboard Gurus and Mentors with us. So let’s go ahead.

Ok so we are here to discuss on this special Women’s Day Program is are the emerging technologies like data science, machine learning and data analytics you know for women. So it will be nice to get your views. Let’s start with you Pavitra, well certainly Yes! That’s because data science and data analytics or the new emerging technology which are coming out are more fun and I think women shouldn’t miss out on it. It’s not just math, it’s also about understanding patterns and intuitive understanding of data and I think women are really well in these technologies.

Interesting point, what about you Chirasmita, what’s your take. I totally concur and absolutely it’s for women. It’s for in fact everybody who has a bent of curiosity it’s for long period of time we didn’t have something as interdisciplinary as data science so people with backgrounds like chemistry, people with backgrounds like economics, statistics, maths, computers, etc, etc, are willing and you know pushing these boundaries because and you know having more diverse workforce means you are thinking in a different manner because there is no one way of solving the problem in data science.

So when you have a diverse background the outcomes are always better definitely Nice insight, diverse background there, very nice. Yes, Lavanya, you are the founder and I think thoughts coming from a founder I am really interested in hearing it go ahead. Absolutely I concur with both of them that they are absolutely for women are you know users of technology where you know data science machine learning data analytics are being applied and not having women take up these fields and work towards the solutions will definitely you know to create very different kind of a solution so I think women are absolutely needed and women are absolutely capable to taking up all these fields.

Nice so we have diversity, we have a founder, I think she is also calling out for you women out there to come and start a company in data science ok please connect with her and learn more from her. Pavitra you are with a growing company and I think how do you see more women joining this kind of field I mean you know what is your thought how do how do we get them to come into these fields So if you see right the last 5-6 years we have had Flipkart, then we have companies like Swiggy, I mean our own desi Indian companies right and the challenges here are diverse it’s not just that you can take a solution that works in the US and put it in here it wouldn’t work for us.

Let’s say for example even in Swiggy how do you find a delivery executive, you are really hungry but by when do you think he will come that is actually some few ML models running to give you that kind of an answer. So, when thing is about women coming here, is also about representation right, it’s just when you are talking about diverse background it’s just not one perspective that matters but for example, let’s say are we talking about gender bias so those things come into picture and it’s it’s data science is also just not about you know biasing us we are only just one solution it’s also looking out different perspectives and when women come into this field those different perspective amplify.

Interesting, what about you Chirasmita do you feel how can we motivate more women to come into this field. I think you have to approach it very differently because what women always feeling you know they are scared of coding test they are scared that they are not at par of you know how I am evaluated
so we change how we evaluate women or you know for per se anybody from a problem-solving point of view vs you know just looking giving them a coding test to do away with their fears then we are looking at them in a different we are putting them in an environment which is easier for them to you know get started so I think for us to revise the pipeline so it’s not like pipeline problem that’s how we approach the pipeline that’s the issue.

And obviously we as companies have a lot of lots of biases so can we improvise our policies better can we have remote work culture for providing flexibility to women at the end of the day it should be like what they bring to the table not you know what they are or who they are. So I think that’s what matters. So you will review them for who they are.

So since you are running a company and I am sure that everybody here is talking about gender, gender diversity and you know that’s the hot topic of discussion in a lot of technology companies then we see that people are trying to change it right what is it that you do at your end to motivate women saying that hey come join us and you know these are the benefits you get or hey come and join these kinds of you know companies come and join data science machine learning field so what is it that you think you should
do what you are doing right now which motivates them to come and join.

Right, I think the first and foremost thing is to help women understand that it’s possible for them to do whatever they want to do right. By showing them role models so being a female founder, for example, is you know give the message that it’s a female-friendly company beyond that I think of the point that it was mentioned by Chirasmita having a flexibility and remote work culture is actually very helpful I think for women because a lot of women actually drop off who are very talented actually who have actually done fantastic things actually drop off from the workforce when they have kids when they go through maternity and giving that flexibility makes a huge difference.

I just wanna add one point here so this is only when I have heard from different women too and it really helps when general sensitization is given the kind of the weightage in a company because what happens is sometimes I have heard this as a feedback given to women not just you know not just in data science community but across saying you come across someone as aggressive right but there is a distinct distinction because there is a difference between an assertive and there is a difference between aggressive so women are perceived to be aggressive when they are actually being assertive.

And when you are a data scientist it’s not like you know you just have one module to complete right you have to defend a data you have to feedback at the same time you also have to it’s also being open enough and taking feedback at the same time also staunchly saying ok this might work and this might not work. So as a whole I see data science community as such if it has to grow we need allies, men allies, allies who are managers who are working with us you know more general sensitized rather than you know it is about enabling women, especially this is only what I have seen in the community.

So like I was saying there is a recent survey which says that there are only 55% of women who actually come into you know the science or the engineering fields and then currently we have only 15 to 22% women in data science and AIML data analytics field so what I would really wanna like to ask you, girls, is you know what really inspired you to come into this field and then how has your journey been.

So Chirasmita let’s start with you, I think I have never been happier I would never have been happier doing anything else apart from this. So I started in data science and I started because I didn’t have any idea what I wanted to do but I wanted to something different like you know everybody wants to do something different so I had that thought and I just join came to Grace Hopper Celebration for Women in Computing and I met a lady there and I just pursued that lady why don’t you teach me? Why don’t you, I want to just learn something and that was my first break and you know first starting point and I got my first job offer post that and probably I had what worked for me was I had been persistent and I was the only data scientist that time in my company for the next 3 jobs actually the only data scientist and now when I see the things have already changed is almost equal almost near equal number of male-female percentage which is like I see there is an improvement but a lot can be done at the same time I think women should be bold to ask questions most people are afraid or scared to ask they don’t ask for a salary they think that I am okay with this because they are judged based on their last salaries right so ideally most company should not even look for salaries if you are the right person for the job you should you know to take that offer.

Most people don’t negotiate women should know how to negotiate through an offer because they deserve it and they shouldn’t question themselves that I am not good enough. So I think those two attitude change can bring a lot of women into this field and it’s a very accepting field because there are lots of opportunities to wear multiple hats in this community basically so I think I love that about my job. Nice, so ask questions be persistent.

Yes, Lavanya you have been, I am sure you had a long journey as machine learning engineer and a scientist then you founded your company so it’s interesting for me to understand and know how did you start and you know what are the challenges that you faced and you know how did you make it through all that.

So my first introduction to data science was never called data science at that time was when I did my masters back in university of Utah So I was working on statistical models for computer graphics so I was using machine learning but it was never called that I mean it was called machine learning but that’s when I got to know about it, it was for computer graphics. So then I worked at amazon again I was actually using some machine learning algorithms but it was never called data science and it was very low-key. So I was an engineer there and I was actually implementing all these algorithms and no word about it right.

So then I was in Myntra for some time and InMobi again there were lot of interesting challenges about ad targeting that can be solved with machine learning and I was looking at all that at that point I felt I wanted to go deeper into it now there are so many resources out there to actually go learn data science machine learning in-depth but at that time even Coursera was not there, it was just starting out. So I decided to do a PhD at that time so I joined the Indian Institute of Science. So I did a PhD at Indian Institute of Science and joined amazon again as a machine learning scientist and researcher.

There I worked again on many interesting problems and I felt at that time there were so many people out there who want to actually become data scientist but would now know how it actually, what a journey is to be a data scientist. How to actually crack the interviews so I started this company machinelearninginterview.com based on popular request from a lot of people on how to prepare for interviews.

So her journey has been long challenging, a lot of learning, unlearning and then relearning again. What about you Pavithra, has it been easy for you? So I used to be a software engineer, I was working in Microsoft then I quit and became design researcher and then I got into innovation consulting, I was with this company called Erehwon and then I had this startup bug wherein let’s do a startup lets do a product and then I met my co-founder and we started working on an AI startup which was you know to create videos that non-designers like us do videos easily.

I was like ok fine now we do know how to coding and anything of that sort but now I figured out that AI was much more interesting and I started into computer vision so it was more about a calling because it was about getting AI and design together. The challenge was immense and it was all about understanding the concepts and getting the concepts to fruition. That was the biggest challenge so I did love doing that and when we got acquired by Swiggy I could see that whatever I had learnt during the startup phase.

I mean there is nothing like being a startup founder because you have to wear several hats so during that phase I learnt a lot and when I came in here I was like wow, this field is far far more interesting and we have lot more challenges here because Swiggy, the challenges that we faced here were very Indian challenges and it’s niche lot of lateral thinking needed to crack them. It’s been an interesting journey, it’s been mostly self-learners like Chirasmita.

I think probably most of the folks in the data science community are, you have to be. So it was always like the thirst of the knowledge, ok this blog is so interesting, let’s see that blog, listen to this person and you know is this something else that you are going to say so it was just like a treasure hunt for us. It was really a wonderful journey. I think the data science community also helped me a lot. So there were a lot of people I was learning things with and I think data science is something that you have to learn together.

Otherwise, it’s difficult and in that case, again right women helping women is something that’s very very important. Once we have VP who recently joined, a women VP, I just see her and get inspired. Women being there actually inspire others. Just like she is a women entrepreneur says it all. Like having a lot of women in leadership roles says a lot about a company. And that’s one of the reasons I love my company and that you see a lot of role models there. So the lack of role models is what needs to be addressed.

When you see an example, you want to be that person, you wan tot take that journey right. I think that’s really important. What I hear is a lot of learning, a lot of persistence, women inspiring women. That’s what I hear commonly from all of you. So it was interesting to hear how you guys became a data scientist, machine learning scientist. What I want to ask next is what opportunities exist in these emerging technologies? So why don’t I start with you Lavanya.

There are many opportunities for women in emerging technologies, almost all the roles that you can think of. Be it a data scientist or data analyst, ML scientist, ML engineers. All these roles are open for women and the field is very encouraging towards women. So companies are taking a lot of care these days to make sure that women are comfortable in joining the workforce and also in retaining the right. And I have personally seen women in many different kinds of roles at different levels. So right from a beginner to like a senior scientist to a head of data science team and I think that definitely answers the question.

There are a lot of opportunities. What about you Pavitra? Do you feel that there are different opportunities available in emerging technologies? It’s also about transitioning from one field to another. Since I had also done something of that sort so one thing is that it’s okay to make mistakes and you can give it a shot and see whether this fits you or not. One thing that I could always say is that you could be a domain expert in a field but data science is again something like a, you can learn quite easily and then apply it in your field and you would be doing wonders with it. You will have like a lot more research that you can show and it’s like a tool you can use.

The next thing is also about AI product managers and I think they play a huge role because they have to understand AI, the uncertainties that come along with it, how do you also tie it to a business metric, how you are going to push the product forward etc. So AI product managers they actually play in different fields and I think that’s another wonderful field. If you have done MBA and if you do a data science course if you are able to understand that why not be an AI Product Manager.

So what we are listening and what we are hearing here is there are a lot of opportunities available. It’s just that how you identify and go for it. So just go ahead and find your space and just do it right. There is also another interesting article that I had read that when there are lesser women in technologies, the product that comes out also kind of become biased. So I want to understand from you Chirasmita that if there more women in emerging technologies will that help the technology that are built.

Also, how does that affect gender equality? Really a good question because representation, inclusion and diversity matter a lot in tech and especially in a place like data science community. The reason being we look for diverse perspectives while solving any problem, any challenge no matter what the role looks like.so the kind of empathy, the kind of very diverse thought process and perspective women bring to the table that can not be challenged for. I think that is what drives the best results out of a company. Most of the companies who are doing really well are today, you can talk of the big googles, Facebooks or amazons of the world, they really care about how the diversity, how the representation is because you see the results. It’s right in front of your eyes. We have stats to prove it.

We do the analysis. So I definitely think that having a gender balance definitely makes a, I think after some time we were talking about this sometime back, about the code that Sheryl Sandberg spoke about that after some time there won’t be female leaders but there will only be leaders. So I think that makes more sense and that would happen eventually for all of us in it together. So we are saying there are opportunities which will also lead to more equality at the workplace and of course we are just requesting more women to go you know and try the emerging technologies. So this women’s day we are actually encouraging a lot of girls, young girls and women out there to come and make a career in the emerging technologies. So I think it will be a good time now for me to ask you, girls, if there are any tips and advice you would want to give the women as to how you how you can make a career in emerging tech.

Great question Revati. This is something I would have told myself when I was growing up that be courageous, be always ready to ask questions. Know what you do not know because awareness of yourself goes a long way and always be relentless because in the beginning this will might look overwhelming but reaching out to a lot of people consistently helps a lot because ninety per cent of the people will never respond to you and the ten per cent is key to your next career move. Be open to reaching out, networking with people and you know be collected always look for role models.

Here all of us today have a journey. So if you need help and advice look up to mentors like us like we also have role models lookup for, for this career transition. Be at it. I think this community needs women. Lavanya, let’s hear it from you. Any tips of advice for women, how can they make a career in emerging technologies? I would say women need to be very proactive with what they want. They need to understand what they want, they should not hesitate to ask, whoever it is. Because sometimes people assume a lot of things so we want men to be sensitised that’s not always the case right.

So you need to be very open and you need to go and ask what you want. The second thing is I think, find your champion, find your role model and find your mentor, can not emphasise enough. Yes mentor Pavitra, let’s hear it from you. What is your advice? AI is the new electricity. That’s what Andrew Young said right. Very soon it’s going to be like ‘People who know AI and People who don’t know AI’. It’s gonna be like data science is going to be like kind of fundamental learning for almost everyone and I think we shouldn’t miss this train and it’s okay to make mistakes, it’s okay to start learning.

I know it’s a little daunting. Let’ say for example you are learning python for the first time, it’s okay just keep at it. You will crack it. This is not a race between you or someone else. It’s actually a race with yourself. It’s more like you know having fun and learning and that’s what matters. Because at the end of the day it’s also about fulfilling what you want to do and I think in that case especially women have seen that they do actually what they really like doing. This is amazing. I think eventually we will not have any women data scientist but we would just have data scientist. Hopefully yes! There won’t be any women data scientist. There is going to be just data scientist.

So you are saying that there are opportunities, it’s just that you keep at it, ask questions, find your mentor and you know don’t be afraid to make a mistake. So thank you all for a very interesting and beautiful discussion. A lot of insights there. Also, I feel that it brings us to the end of our discussion today. I would like to close out by a very beautiful quote by Estee Lauder “I didn’t get here by just wishing for it and hoping for it. Actually I actually reached where I reached by working on it.” So I think that goes for all the women out there in the emerging technologies. So wishing you all a very happy women’s day. Wishing you all a very happy women’s day. Thank you, thank you and all the viewers out there, happy women’s day. Happy women’s day to everybody out there.

 

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reference – Are Emerging Technologies for Women? How to Become a Data Scientist Data Analyst or AI/ML Engineer?

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