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

there are many opportunities for women in emerging technologies almost all roles that you can think of the data scientist or a data analyst right or a neuroscientist a male engineer all these roles are open from the name and the field is very encouraging towards women hey guys so this Women’s Day we have a very special video where we’re going to talk about are the emerging technologies for women so emerging technologies we mean AI ml data science and data analytics and to discuss this we have some great springboard gurus and mentors with us so let’s go ahead come let’s meet our mentors and gurus at Springwood we have Shira Smitha who is the data scientist at g2 we have lavender who is the founder and machine learning scientist at machine learning interview calm and then we have Pavitra of course with us who’s the staff research scientist at sweety welcome girls thank you for having us thank you for everything thank you good so before we get into the discussion for today please subscribe to springboard India YouTube channel for more interesting discussions and insights into machine learning data science and data analytics ok so we are here to discuss on this special Women’s Day program is our the emerging technologies like data science machine learning and data analytics you know for women so it’ll be nice to get your views let’s start with you thorough job well certainly yes that’s because so data science and data analytics or the new emerging technologies that are coming up 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 not really well in these technologies interesting point what about uterus with Orion totally conquered and absolutely it’s for women it’s for in fact everybody who has a bend of curiosity it’s for a long period of time we didn’t have something as interdisciplinary as data science so people with backgrounds like chemistry people with backgrounds like economic statistics maths computer exit exit willing and you know pushing these boundaries because and and you know having more diverse workforce means you’re thinking in a different manner because there is no one way of solving a problem in data science so when you have a diverse background the outcomes all these are better definitely it’s nice inside diverse background a very nice yes lavender you’re the founder and I think thoughts coming from a founder I’m really interested in hearing it absolutely I concur with both of them that they are absolutely for women 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 these solutions we’ll definitely you know create a very different kind of a solution so I think women are absolutely needed and women are absolutely capable of taking up all these fields nice so we have diversity we have a founder I think and she’s also calling out for you women out there to come and start a company in data science okay 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 these kind of fields I mean you know what is your thought how did you how do we get them to come into this field so if you see right like the last five six years we have an e-commerce companies my flip card and then we have companies like swiggy I mean um one day see Indian companies right and the challenges here are type words it’s not just that you can take a solution that works in US and put it in here it wouldn’t work for us let’s say for example the events we need like how do you find you need anybody execute if you’re really hungry but when do you think you would come that is actually some few ml models running he’ll give you that kind of so when the thing is about Tremont coming here is also about representation right it’s this that when you’re talking about diverse backgrounds it’s just not one perspective that matters but for example and say are we talking about gender bias so those things come into picture and it’s it’s it’s it’s data science is also just not about you know biasing us with just one lis one solution it’s also we’re looking at different perspectives and when women come into this field those different perspectives amplified interesting what about uterus method 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 feel like you know they’re scared of coding tests they’re scared that they are not at power 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 versus you know just looking giving them a coding test to do away with their fears then we are looking at them in a different way we are putting them in an environment which is easier for them to even get started so I think it’s for us to revise the pipeline so it’s not like a pipeline problem it’s how we approach that 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 removed work culture you know providing flexibility to women at the end of the day it should be like what they bring to the table not you know where what they are or who they are so I think that’s what matters so you review them for who they are so since you are running a company and I’m 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 and we see that and people are trying to change it right what is it that you do at your end you know to motivate women saying that hey come join us and you know these are the benefits you get or hey come and join these kind of you know companies come and join data science machine learning field so what is it that you think you should do what you’re doing right now which motivates them to come in join me right I think the first and foremost thing is to help them and understand that you know it’s possible for them to do whatever they want to do right by showing them good role models so being a female founder for example is you know give the message that it’s a female friendly company here right beyond that I think one of the points that was mentioned by Childress Mehta having flexibility and remote work culture is actually very helpful I think for women because lot of women actually drop off for very talented actually who have actually done fantastic things I actually drop off from the workforce when they have kids when they go through my turn ad and giving that flexibility makes a huge difference so I just want to add one point here so this is something that I’ve heard from little women too and it really helps when gender sensitization is given that kind of a vintage in a company because what happens is sometimes I’ve heard this as a feedback given to women not just you know not just in the data science community but recruits across saying you come across as someone as aggressive right yeah but there is a distinct distinction because there is a difference between being assertive and there’s a difference between being aggressive so women have perceived to be of deserve when they are actually just being assertive and then you are a data scientist it’s not like you know you just have one module to complete right you have two different the data you take feedback at the same time you also have to it’s also being you know open enough and taking it feedback at the same time also starts the saying okay this might work on this might not work so as far as a whole I see data science community as such if it has to grow we need allies main allies allies who are our managers who are working with us you know more generous and rather than you know it’s about enabling women especially but this is money that I’ve seen her community so like I was saying there’s a recent survey which says that there are only 35 percent of women who actually come into you know we science or the engineering fields and then currently we have only 15 to 22 percent women in the data science and AML data analytics field so what I would really want to ask you girls is you know what really inspired you to come into this field and then how has your journey been so just whether let’s start with you I think I have no been happier I would never have been happier doing anything else about 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 do something different like you know everybody wants to do something different so I had that thought and I just came to Grace Hopper celebration for women in computing okay and I met a lady there and I just pursued that lady why don’t you teach him why didn’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 at that time in my company for the next three jobs actually the only data scientist and now when I see it’s the things have quality change

it’s like almost equal or 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’m ok with this because they are judged based on the last salaries right so ideally most company shouldn’t even look at salaries if you are right person for the job you should just you know take that offer so most people don’t negotiate so women should have 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 lot of lots of opportunity to wear multiple hats and to end this community basically so I think I love that about my job nice ask questions be persistent yes well now you’ve been I’m sure you had a long journey as machine learning engineer and scientist and then you found it a 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 how do you make it through all that right so my first introduction to data science it was never called data science at that time was when I did my Master’s 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 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 in Mobe again there were a lot of interesting challenges about ad targeting that can be solved with machine learning and I was looking at all that and 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 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 then joined Amazon again as a machine learning scientist and researcher right so there I worked again on many interesting problems and I felt at that point that there were so many people out there who want to actually become data scientists but who do not know how it actually what the journey is to become a data scientist and how to actually crack the interviews yeah so I started this company machine learning EUCOM mais non actually popular request from lot of people or you know how to prepare for interviews so hurt already has been long challenging lot of learning and learning and then relearning again what about you pakistan has it been easy for you so i used to be a software engineer I was working in Microsoft and then I quit and I became a design to search and then I call it the innovation consulting where I was a company called as l1 and then I had a started bug where and you know what let’s do a start-up as to a product so I met my co-founder and then we started working on a I startup which was like you know creating videos that non designers like us can do these videos easily so I was like ok fine now or never will still do low coding and I think I Mossad but then 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 and the challenge was immense and it was all about understanding the concepts and you know getting the concepts to enough tradition like that was the biggest challenge so I really loved doing that and when we got acquired by sweetie I could see that what am I at learned during the startup phase I mean there’s nothing like being a start-up founder because you have to wear several hats so during that phase I bought a lot and when I came in here I was like wow this field is far far more interesting and we have not more challenges here because swiggy the the challenge that we face are very Indian challenges and it’s now needs a lot of lateral thinking so it’s been an interesting journey hype it’s been mostly a self rather was lecture stuff yes I think probably most of the folks in data science community are self you have to be yeah it’s actually right so I mean there’s always a thirst for knowledge right and play ok this blog is going to stay in a state that involves let’s go listen to this person and you know is this something else that you’re going to say so it was just like you know Natasha and for us yeah right it was a really even wonderful journey and I think the genasense community also helped me a lot so there people I was learning things with and I think there isn’t something that you have to learn together

yes otherwise it’s difficult and in that case again right women helping women it’s something that’s very very important and once you see let’s say I have a VP who simply joined a woman VPN I just see her and I get inspired right a woman being there actually inspires others just like you know she is a woman entrepreneur yeah like having a lot of women in leadership roles says a lot about a comfort zone and that’s one of the reason I love my company that you see a lot of role models there so the lack of role models is what we have and it needs to be addressed when you see an example you want to be that person you want to take that journey right I think that’s really important in it so what I hear is a lot of learning a lot of persistence women inspiring women you know that’s that’s what I hear commonly a tamale yeah so it was interesting to hear how you guys actually became reader scientists and machine learning scientists whatever the ask next is what opportunities exist in these emerging technologies for women so when do I start with you know there are many opportunities for women in emerging technologies almost all roles that you can think of big data scientist or a data analyst right or any scientist I’m an engineer all these roles are open for women and the field is very encouraging towards women right so companies are actually taking a lot of care these days to make sure that you know women are comfortable in joining the workforce and also in retaining them right and I have personally seen women in many different kinds of flows and different levels so right from a beginner to like a senior scientist to a head of a data science team and I think that definitely answers the question has the other so there are a lot of opportunities available what about your power I feel that you know there are different opportunities available in this emerging technology so it’s also about transitioning from one field to another so since I had also done something of that sort so one thing is that that you it’s okay to make mistakes and you can give it a shot and try and see even this fits you or not one thing that I could always say is that when you you could be a domain expert in a field but design in this again something like you can learn quite easily and then apply it in your field and you would be doing wonders with it you have like a lot more results that you can show and it’s like another tool that you can use the next thing is also about AI product managers and I think they play a huge role because they are to understand AI the uncertainties that come along with it and how do you also you know tie it to a business metric how are you going to push the product forward etc right so a product managers they they actually play in different fields and I think that’s all the wonderful thing that you know if you have done a MBA and if you do a data science course or if you’re able to understand that why not be a problem so what we are listening or what we are hearing here is that there’s 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 you know just to each other there’s also another interesting you know article that I’d read that when there are lesser women you know in technologies the product that come out also you know kind of become biased so I want to understand from your chourus method is that if there are more women in emerging technologies will that help you know the technology that are built also how does that affect the gender equality definitely I mean really a good question because representation inclusion and diversity matters a lot in tech and especially in a place like a data science community the reason being we look for diverse perspectives while solving any problem any challenge no matter what the rule looks like so the kind of empathy or the kind of you know very diverse thought process of perspective that women bring to the table that cannot be challenged for and I think that that is what drives the best results out of a company so most of the companies who are doing really well today you can talk of the big Google’s Facebook’s or Amazon’s of the world a company like g2 so we they are do early you know care up care about you know the how the diversity how the representation is because you see the results and it’s right in front of you is there we have stats to prove it so we do the analysis so I definitely think that having a gender balance definitely makes you know it’s I think after some time like I think we were talking about this some time 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 all of us are in it together so we are saying that there are opportunities it will also lend to more equality at workplace and of course we’re just requesting more women to go and you know try the emerging technologies so this Women’s Day we are actually encouraging a lot of girls young girls and women out there you know to come and make a career and the emerging technologies so I think it would be a good time now for me to ask your girls if there are any tips and advice you would want to keep the women as how you can make a career in emerging tech like great question or everything like 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 so that and always be relentless because in the beginning this field might look overwhelming but reaching out to a lot of people consistently helps a lot because 90% of the people will never respond to you and that 10% is your key to your next career move so be open to reaching out network with networking with people and you know like be connected always look for role models here all of us today have a journey so if you need help and advice look up two mentors like us like me you also have role models to look up or fall for this career transition so be it I think this community needs women that’s Lavanya let’s hear it from you any tips and advice for women how can they make a career and the emerging technologies right I would say women need to be very proactive with what they want they need to understand what they want and they should not hesitate to ask whoever it is because sometimes people assume a lot of things so we want men to be sensitised but that’s not always the case right so you need to be very open and you need to go and ask what you want second thing is I think find your champion find your role model and find your metric cannot emphasize enough so yes mentor Pavitra let’s hear it from you what’s your advice ai is a new electricity that’s what I’m going G said right and very soon it’s going to be like people who know I have people who don’t know anyway so it it’s going to be like data science is going to be like kind of a fundamental learning for most everyone and I think we shouldn’t miss this train and it’s it’s okay to make mistakes it’s it’s okay to start learning I know it’s a little daunting but let’s say for example you’re learning writing for the first time it’s okay just keep at it right you will crack it and this is not a race that is between you and someone else it’s actually a race with yourself if you it’s more like you know having fun and learning and that’s what matters because it’s 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 you know they mean they actually do what they really like doing this is amazing I think eventually they will not have any women data scientist I think we are saying that there are opportunities is just that you keep at it ask questions find your mint and you know don’t be afraid to make a mistake yeah so thank you all for a very interesting and beautiful you know discussion a lot of insights there also I feel that you know it brings us to the end of our discussion for today and I think I’d like to close it by a very beautiful quote by Estelle order she said you know I didn’t get here by just wishing for it and hoping for it I actually reached where at least by working on it so I think that goes for all the women out there in emerging technologies it’s so wishing you all a very happy Women’s Day wishing you all a very happy when enjoy yours out there happy Women’s Day happy Women’s Day to everybody out there thank you for watching this video please like it and please subscribe to springboard India youtube channel thank you