Explore Data Science as a Career with Anita Sachdeva

[00:00:00] Hello and welcome back to Wisdom Whispers with Abhishek Mittal. Today we have a true data

[00:00:05] powerhouse with us, Anita Sachdeva. She is a leading data science expert with close to

[00:00:11] two decades of experience. She has a proven track record of driving innovative and measurable

[00:00:18] results through her work across organizations. So let's welcome Anita to the show. Hi

[00:00:23] Anita, welcome to the show. Thank you Abhishek. Thank you for having me here. Pleasure is

[00:00:27] all mine, Anita. Before we get into the topic, it's an interesting topic, data analysis,

[00:00:34] data science is so much in right now. First of all, how is your career journey? Thank

[00:00:40] you. So my career journey starts with my love for maths. I think from the very beginning

[00:00:48] probably 4th, 5th or 6th somewhere, I always had love for maths because you never need

[00:00:56] to remember it, right? You don't have to remember anything. You just need to remember it.

[00:01:00] If you know the concept, then you must have done the whole paper, right? So my mother

[00:01:04] always used to whenever I was in cousin's place, right? So she would teach maths,

[00:01:09] she would teach maths. So everyone knew that Anita loves maths a lot. So that is

[00:01:14] your, that took me towards this career of analytics and this career, starting the

[00:01:20] career into analytics was not that straightforward as it seems to be right now because

[00:01:24] I come from a background where a female is not supposed to earn. You don't want to earn.

[00:01:31] You just study as much as you want. Parents are very much invested in that. You study

[00:01:35] as much as you want. But you don't have to earn. That was the point. So I kept

[00:01:41] on doing science lead, 12th Karli, got good marks, then went to take admission in engineering.

[00:01:47] They are sitting there in counselling and parents are saying no because they were

[00:01:51] at a college, they didn't want me to go. So they said you will do it here. And we

[00:01:57] got up from counselling and came home. So I thought it's okay that you are not doing

[00:02:01] engineering because it was a dream. So let's do maths. So Delhi University, I'll pursue

[00:02:06] the maths. Okay. Okay after 12th. Now if you wanted to pursue maths, then that is

[00:02:11] where there was a college in Gargi College, Delhi University. There were no maths

[00:02:16] honours. Maybe not even today. But there were physics honours. And for maths honours,

[00:02:21] you had to go far away plus you had to go to a co-ed college. That is where parents

[00:02:24] didn't want me to go again. Okay. So I said okay, no one. Physics is the closest to maths.

[00:02:29] That is where let's start our career. So that is where I got into maths, sorry, physics

[00:02:34] honours in Gargi College. After that, MSc was another majority of the girls in

[00:02:39] department of physics and astrophysics in Delhi University were girls. So let's

[00:02:44] go further. It was a little far away but there was no option. So MSc was done. Now along

[00:02:48] with that, what we will do after this because at that time there was no campus placement

[00:02:52] in MSc. At that time, where is the campus placement going? Exactly. There was no

[00:02:58] place. So what we will do now? So I didn't know what to do after this. What

[00:03:02] would be the job? How would it be? I didn't know anything. My friend used to

[00:03:07] have a, she was from the government background. Her father was into government. So she

[00:03:10] used to tell me when we used to come home from the bus, that the papers are on the

[00:03:14] side, they take a form from there and apply it. So you go to the government job. I said

[00:03:19] okay, you keep guiding me and we will go like this. Okay. But what happened was that

[00:03:24] during the course, I got to know that there is a gate exam in which you

[00:03:29] appeared and you can get into IIT again. The dream was five years ago. You

[00:03:34] can complete it now. I said let's try it. Now for 15 days, there are

[00:03:38] holidays in DU, there are winter vacations for 15 days. So that time,

[00:03:42] finally in MSc, my friends were going to Goa. Now parents will allow me to go

[00:03:47] to Goa. So I didn't go to Goa, went to Chandni Chowk and bought a book

[00:03:51] of the gate. She studied for 10-12 days. And did some more. And then in January,

[00:03:56] I think there was an exam somewhere. She gave the exam and I got through.

[00:04:00] Lovely. Lovely. Now after this, the result of the gate exam has come. You have

[00:04:05] got through that. Now what do I have to do? Now I have to apply. Now I have to

[00:04:09] stay away from home to apply again because I have to stay at home. Right? So it has always

[00:04:12] come that I have to stay at home. There is a little environment where it is better

[00:04:16] if there is no co-ed. But now there was no option. So I applied only in IIT,

[00:04:20] Delhi. So that is where I got into that I went to IIT, Delhi, applied,

[00:04:24] MTEG, applied optics. I got into. Okay, that's done. Now two years have passed.

[00:04:30] I have developed a different level of confidence. Because when you get into IIT,

[00:04:35] you feel that you should definitely come into life. Because that changes your entire,

[00:04:41] I would say the mindset that you can do anything in this world. So I think I had that privilege

[00:04:46] of going there at least five years later itself. Which is okay. And then after that,

[00:04:51] when there is a campus placement, that is where I would say that my campus placement

[00:04:55] was in a very different way because the background of our profile was expected

[00:05:01] that he will get into a core job. But my entire batch in fact went into a core job.

[00:05:06] I was the only one who got into analytics and consulting. And I think I was destined to be

[00:05:11] part of that maths for my career, entire career. And I got that. I was fortunate enough,

[00:05:17] I would say, and thanks to God and thanks to Almighty that I got into analytics. And

[00:05:20] that is how my career journey started in a campus placement. But for that, I had to go outside.

[00:05:26] I went to Chennai for a little more than a year and then came back because as I was supposed to

[00:05:30] come back to my home. And after that, I continued my career in Goh Dugan in a couple of organizations.

[00:05:37] So that's how I started my career. Great. That's such a journey, right?

[00:05:44] But it was beautiful. Absolutely. Because you have different experience at different places.

[00:05:49] Yeah. And also your love towards maths, right? Towards data is finally now you got it.

[00:05:55] You know, Shah Rukh Khan's dialogue is that he has tried to get you mixed up with him.

[00:06:01] So whatever you want happens. So I think persistence is the key.

[00:06:06] You continue to love what you love and do for it. Just try for it. You will get it.

[00:06:12] Nice. So now because your career is all throughout in data analytics. So when you started

[00:06:18] your journey in data analytics, what was the expectations?

[00:06:22] And now really, what we do when we start because it's a job profile.

[00:06:29] A JD is given to the kids, right? And then they get into a job and then they see that

[00:06:33] oh there is a difference between what is job description and what I am really doing.

[00:06:36] So can we bridge that gap? That he really did what he did there.

[00:06:41] And what did you do initially? See with this social media and YouTube and all that,

[00:06:48] people are much more aware today. But at our times, I didn't know anything.

[00:06:53] Analytics is just that I knew that I have to do something like consulting.

[00:06:57] So analytics consulting is the best place to go to.

[00:07:00] So expectations were nothing. I knew that you will get maths here.

[00:07:05] And you will not have to go into it.

[00:07:08] Core thing that you are not studying optics, you are not studying physics and you will be away from that.

[00:07:12] So that was the only thing. Expectation was nothing. At least for me.

[00:07:16] Maybe other people were more aware but I think at my time, I didn't know much about kids

[00:07:20] when they were getting into your jobs. But coming on to the true reality, what happens there?

[00:07:26] I think I was fortunate to start my career with an organization which is a putti consulting firm

[00:07:31] more like a very small startup firm. We were about, I think, not even 100 people.

[00:07:38] So that is where I got to learn everything of a project, analytics project from just data

[00:07:47] exploration, data cleansing to defining the problem statement and then getting on to building

[00:07:53] the predictive model and recommendation also to the customer. So end to end of that.

[00:07:58] And that was just because I was part of a small startup firm. Otherwise,

[00:08:03] if you go to big companies, you are assigned a specific task and that is what you do.

[00:08:07] So I think when somebody is starting a career, I feel like if they get to have a chance to

[00:08:11] start their career with a startup, nothing like it. I would say you will be blessed with it

[00:08:17] because that is the time you need to learn. You have a lot of time. You should learn

[00:08:22] and you get to learn. Grilling is also a lot.

[00:08:24] Startup is a lot because you are directly working with the founder. There are no layers between you.

[00:08:28] There are no layers. So you know the client's requirement and the founder knows it too

[00:08:33] and you both are working collaboratively. That's why.

[00:08:35] That's right. But now again, in the real right now in the world, people are a little scared

[00:08:41] of startups. Recently, a lot of startups who are right. So people are not looking into

[00:08:46] startups when people are getting into good corporates. So what do they really start?

[00:08:51] Okay. So being into multiple organizations, I would say, I myself have recruited campus hires,

[00:09:00] have recruited lateral recruit. Apart from that, when the campus hires come on board,

[00:09:05] we train them. So in organization there are specific trainings which happen for them.

[00:09:11] And I would say when I started at that time, there was a training there,

[00:09:15] a SaaS training. Statistical analysis software because that was the bread and butter for all

[00:09:19] the analytics people in the world, I would say. And that was one of the expenses of the

[00:09:24] but SaaS training was given. Basic statistical skills training was given.

[00:09:30] And I would say going to building a basic model of logistic regression so that

[00:09:35] they can build it all. Because they were built from everyone at that point in time.

[00:09:40] That was the need because the result that came out of that,

[00:09:42] then we were giving the insights and the recommendation to our customers.

[00:09:47] So basic things were taught. And then rest of the stuff you get to learn on the go,

[00:09:52] on the job, like you are Excel. Excel doesn't teach you anything. You learn.

[00:09:56] But so these were some of the basic things which if you know them,

[00:10:00] they are very good. If you didn't know them, you would have been taught.

[00:10:02] I would say. And then your journey starts. Then you are given a project,

[00:10:05] you are given a real-time project. So there can be no logistic model.

[00:10:08] There can be a gamma model, a log-normal model because you are working on

[00:10:11] the insurance, severity models, survival analysis.

[00:10:14] I don't understand anything but still.

[00:10:16] So there are many different kinds of techniques. So it depends on which problem statement you

[00:10:20] are working on, which industry you are working on, which domain you are working on.

[00:10:22] Accordingly your predictive models vary, your machine learning algorithms vary.

[00:10:26] So you learn on the go.

[00:10:28] So primarily when someone new is joining in the team, at least

[00:10:33] they should know the basic fundamentals that are taught in college.

[00:10:37] Right? They should revise them and come.

[00:10:39] Apart from that, Excel. If they can excel in that MS Office, MS Excel, then it will be great.

[00:10:46] Right? It's a good complementary skill which they should have.

[00:10:50] When you complete your project after you complete your case studies,

[00:10:53] you create presentations also. Right?

[00:10:54] So even if you have these skills,

[00:10:57] you are a holistic developed person already.

[00:11:00] Okay.

[00:11:00] But if you don't have them, you get to learn on the job.

[00:11:03] And now I will say what you are saying that what was at that time,

[00:11:07] in today's date, I would say it is Python.

[00:11:09] If you know Python, then you can do a lot of things.

[00:11:12] You can't just work with analytics. Right?

[00:11:14] You can develop web, you can do web scrapping.

[00:11:18] But if you know Python, then there is no end.

[00:11:20] That just starts your beginning into the world of technical emerging tech, I would say.

[00:11:25] So whether you are from JNAA or LLM,

[00:11:28] if you know Python, then you can start your journey on a very,

[00:11:32] I would say you will be able to develop yourself

[00:11:35] and you'll be able to establish yourself very quickly in the organization

[00:11:39] because everything is based on Python.

[00:11:41] Good.

[00:11:42] So a little tricky question.

[00:11:46] Now, if you have to go back to 23-year-old self of yours,

[00:11:50] right?

[00:11:56] Okay.

[00:11:57] So I think I feel that when I started my career,

[00:12:01] I learned a lot of technical skills from people around me.

[00:12:03] And being in the startup, you learn a lot of technical skills because you need to deliver on the project.

[00:12:08] But if you are starting with a small firm, then you have softer skills,

[00:12:11] a little leadership aspect, leadership skills.

[00:12:14] You don't have that much focus on it.

[00:12:17] I think if you have that parallel focus on that as well,

[00:12:21] you will be able to quickly move up in the career in your trajectory.

[00:12:26] So I think I feel that if I start my career as a 23-year-old,

[00:12:32] I would want to focus on the L side as well, the leadership side as well equally.

[00:12:38] Right.

[00:12:38] I think all of my guests, I and Sabne,

[00:12:41] said that in today's world,

[00:12:43] you can coach yourself if you have softer aspects.

[00:12:47] If it's not that, then it becomes very difficult.

[00:12:50] Right.

[00:12:50] So you rightly pointed out that.

[00:12:52] Now, if you look back and when you started,

[00:12:55] and when someone is starting now,

[00:12:58] what do you think these people should focus on when they are getting into an organization?

[00:13:04] So as I said, when I started on, there was no expectation,

[00:13:07] nothing was there.

[00:13:07] We were taught on the job.

[00:13:09] But today when people join,

[00:13:12] the campus hires comes in or lateral comes in,

[00:13:15] they are very much aware of what they have to do.

[00:13:18] But I think if I were to give some advice,

[00:13:20] what is the need in our field?

[00:13:23] So I think as I said, I think there is one more thing as data visualization.

[00:13:29] If you have that as another skill,

[00:13:32] it can be tabloid, it can be power beer, it can be any other,

[00:13:35] probably you can develop the dashboard in Python itself.

[00:13:38] That is also there.

[00:13:40] So if you have any of those skills,

[00:13:41] I think that means you're good combination of skills to start your career

[00:13:46] into any corporate analytics organization.

[00:13:49] So this is when I'm starting also.

[00:13:53] But if I have to be successful,

[00:13:56] I have started now,

[00:13:57] but if I have to be successful in a long-term career path within data analytics,

[00:14:01] what are additional skills which I need to focus on?

[00:14:04] If you really want to be successful,

[00:14:06] first is your zeal to learn,

[00:14:10] your continuous learning on the job,

[00:14:13] because if you are in an organization,

[00:14:16] and you are doing well,

[00:14:19] your leader sees you as somebody who's in high potential,

[00:14:23] I can tell you that you will be planted,

[00:14:25] literally planted on different kind of projects,

[00:14:28] challenging projects wherein you don't have the expertise,

[00:14:31] but you still need to deliver on it.

[00:14:33] And to deliver on to that, if you are not learning,

[00:14:37] you cannot deliver.

[00:14:38] So first most important thing is that continuously evolve yourself

[00:14:43] because in this rapid change of industry,

[00:14:46] if you're not evolving,

[00:14:47] if you're not learning,

[00:14:49] you can't be with the pace

[00:14:50] and you can't continue to be the hypo for your organization,

[00:14:53] for your leader.

[00:14:55] So I would say that is the first thing that you need to have in you

[00:14:58] because when I look back myself,

[00:15:01] as I said, I didn't have any skill,

[00:15:03] I mean basic statistical skills,

[00:15:04] I studied probably in 10th or 12th,

[00:15:06] that is where I brought in.

[00:15:07] SAS, I learned on the go, on the job.

[00:15:14] For example, I learned the K-means clustering.

[00:15:16] That was a very big thing at that point in time

[00:15:18] when I learned it in 2007 itself,

[00:15:20] that with my first job itself, I learned it.

[00:15:23] Very few people knew the backend concept of it.

[00:15:26] I got to learn because of somebody along with me

[00:15:29] who was really a great person on to it,

[00:15:31] great skilled person on to it.

[00:15:33] After that, I kept on coming into different assignments.

[00:15:36] I was given different kind of assignments

[00:15:37] into new organization as well,

[00:15:40] wherein I put in a lot of time on new techniques like NLP.

[00:15:45] Now people know NLP a lot.

[00:15:47] I would say I worked on NLP in the organization 10 years back,

[00:15:51] about eight, nine years back.

[00:15:53] That was a very big thing, text mining, sentiment analysis,

[00:15:56] image analytics.

[00:15:58] Then I did OCR, which is optical character recognition.

[00:16:03] So I did all sorts of things, time series forecasting.

[00:16:06] And it was given because

[00:16:08] leader was invested in me.

[00:16:11] Leader knew that he will do this girl.

[00:16:13] He will do this and I will give him something different.

[00:16:15] That too he will do it.

[00:16:17] So that is your, if you are learning and you are delivering,

[00:16:19] your passion is what the organization sees.

[00:16:22] I think you need to bring your full self every day to the office,

[00:16:26] to your job.

[00:16:27] If you are bringing your full self,

[00:16:29] I think you'll be given different kind of responsibilities

[00:16:32] and you'll end up being a successful

[00:16:36] person and established leader in your organization.

[00:16:39] Nice.

[00:16:40] So continuously we talk about this.

[00:16:42] One more thing, in the last 17 years,

[00:16:44] we have done so many data analysis.

[00:16:46] There is a surprise analysis that came up

[00:16:49] which you thought, I didn't think of it.

[00:16:55] You came up with such an analysis when you did your work.

[00:16:58] So as I said, 10 years ago,

[00:17:01] I worked on a problem statement which was

[00:17:03] natural language processing.

[00:17:04] What was there that the data of calls came up?

[00:17:07] Like an insurance company,

[00:17:09] they used to receive calls in the call center

[00:17:11] when there was a claim.

[00:17:13] So they used to receive calls.

[00:17:14] There was incoming and outgoing calls.

[00:17:17] So the customer was so worried that

[00:17:19] there are so many incoming calls

[00:17:21] that it becomes very difficult for us to manage.

[00:17:23] So can you study this data for us?

[00:17:25] So first of all, it was speech data.

[00:17:27] So speech was converted into text.

[00:17:29] That was the first thing.

[00:17:30] And the problem statement was not clear

[00:17:32] what the customer wanted.

[00:17:33] He just caught the data.

[00:17:35] You will see what speech data can do.

[00:17:38] So that is where defining the problem statement

[00:17:40] becomes a very big thing in analytics

[00:17:42] because the customer doesn't know what to do.

[00:17:45] So if you are not able to clearly tell the story

[00:17:49] on defining the problem skills,

[00:17:51] your customer doesn't understand anything.

[00:17:54] So what happened there?

[00:17:56] We took on that data, converted into text.

[00:17:59] Now we put clustering on what was done with the text.

[00:18:03] As I learned the clustering.

[00:18:05] So and then we tried to do sentiment analysis on that.

[00:18:10] That did not go well as such.

[00:18:12] But when we clustered, we knew

[00:18:15] the first observation was that

[00:18:17] 85% of the calls were incoming

[00:18:19] and 15% were outgoing.

[00:18:21] That means that customers are reaching out to you.

[00:18:26] They are telling you the claim

[00:18:27] but you are not informing them enough.

[00:18:30] So if you inform them more proactively,

[00:18:34] they will not reach out to you.

[00:18:35] Your incoming calls will be reduced.

[00:18:38] In incoming calls, we had to study what these are.

[00:18:41] So first of all, this was surprising for the customer

[00:18:45] that 85% were incoming calls and 15% were outgoing calls.

[00:18:49] So this was a big insight for us as well.

[00:18:53] Why is this happening?

[00:18:54] But it very well resonated with the customer

[00:18:56] and that customer was happy.

[00:18:58] And then we went on to tell

[00:19:00] which type of incoming calls are coming.

[00:19:02] Most of the calls are auto damaged.

[00:19:05] So what type of repair is coming?

[00:19:07] What types of calls can you reduce?

[00:19:09] Sitting at here itself, you can settle the claim

[00:19:11] versus going to the field and then settling the claim.

[00:19:14] So if there's a small claim, you will settle it on the call.

[00:19:16] If there's a big claim, you will send it to someone.

[00:19:18] So those kind of recommendations we built in along with that.

[00:19:21] Superb.

[00:19:23] Building upon on this,

[00:19:24] you touched upon on AI, GenAI, right

[00:19:27] that how this is evolving in your industry

[00:19:30] and it is these are now not just buzzwords

[00:19:34] because this is now in application.

[00:19:36] This is real.

[00:19:37] This is real.

[00:19:37] This is real.

[00:19:38] Right?

[00:19:38] It was a buzzword, but it's not a buzzword.

[00:19:40] It's coming.

[00:19:41] It's coming.

[00:19:42] It's already here.

[00:19:43] It's already here.

[00:19:43] It's already here.

[00:19:44] It's also eaten.

[00:19:46] So it's coming.

[00:19:47] So what's going to happen next?

[00:19:48] In your industry in particular,

[00:19:50] what are the productivity challenges

[00:19:53] or productivity improvements

[00:19:54] that will come and how you look up to GenAI and AIS?

[00:19:57] As you said,

[00:20:00] that many jobs have come and gone.

[00:20:02] Right?

[00:20:02] And actually I attended one of the conferences

[00:20:08] in which everybody was talking about GenAI.

[00:20:12] But nobody has ever ridden a real boat.

[00:20:17] I mean, I say that success has not been achieved.

[00:20:19] Sorry.

[00:20:20] The journey starts.

[00:20:22] But what has been outputted from that?

[00:20:24] Everybody is trying to that.

[00:20:26] So, the same is here.

[00:20:28] We are trying to use cases.

[00:20:30] Whether we are GenAI's chat GPT

[00:20:34] or touching on different tasks.

[00:20:37] For example, if I were to say,

[00:20:38] we are looking onto two different use cases.

[00:20:40] One for the customer and another for the colleagues.

[00:20:42] If I say for the customer,

[00:20:44] then it's our job to generate insights from data.

[00:20:47] Right?

[00:20:48] So how can we generate auto insights?

[00:20:52] I mean, the customer asks a question and we answer on to that directly.

[00:20:56] I mean, I don't have to go.

[00:20:58] I just make the data and give it to the customer.

[00:21:00] And that chat GPT type something,

[00:21:02] the customer gets it and solves their problem end to end.

[00:21:06] The time gap will be reduced

[00:21:08] because our partners are usually on-sho.

[00:21:10] So what will happen to them?

[00:21:12] They will have things immediately available.

[00:21:14] Plus, if we say that someone comes to the ad,

[00:21:16] they will say that they will deliver for 2-3 days.

[00:21:18] It won't happen.

[00:21:19] So, speed to market will increase.

[00:21:21] Right?

[00:21:22] So, it will generate a lot of efficiency.

[00:21:24] We will not need these many people.

[00:21:26] We will not need...

[00:21:28] We'll have on the go and quicker insights.

[00:21:30] One thing happened.

[00:21:32] The other thing happened on the colleague aspect.

[00:21:34] What we are looking at is

[00:21:36] because it's not necessary that everyone is your Python trained.

[00:21:38] As I said, we started as an industry

[00:21:40] which was a SAS trained and then we moved on to Python.

[00:21:42] And there will be no other language like this sooner.

[00:21:44] So everybody can't get onto that

[00:21:46] that I am an expert today.

[00:21:48] So that is where

[00:21:50] if I solve the problem of

[00:21:52] creating the code from the JNAI

[00:21:54] that helps my colleagues.

[00:21:56] Because you learn basics

[00:21:58] but you don't learn from basics

[00:22:00] that you can optimize things.

[00:22:02] You can modify the code

[00:22:04] but write it from scratch.

[00:22:06] That's a task.

[00:22:07] So, if I provide them with a platform

[00:22:09] JNAI type

[00:22:11] and if you ask the chat GPT

[00:22:13] that will give you code.

[00:22:15] So something similar we are thinking

[00:22:17] these are the two use cases

[00:22:19] that we have a better efficiency for colleagues as well

[00:22:21] and we are able to

[00:22:23] enable better business value

[00:22:25] for our customers

[00:22:27] So, now when someone is new

[00:22:29] you said that we shouldn't have Python

[00:22:31] because Python is related with

[00:22:33] AI and JNAI

[00:22:35] So, what should be the preparedness

[00:22:37] when they are coming in a particular role

[00:22:39] to understand

[00:22:41] that JNAI and AI

[00:22:43] are important

[00:22:45] and what I need to do for this

[00:22:48] is to make sure that you can throw some light on that.

[00:22:50] So, for that

[00:22:52] you should first do courses

[00:22:54] basic understanding of what JNAI

[00:22:56] and how it holds an importance

[00:22:58] for us

[00:23:00] into the real world of analytics

[00:23:02] of insights

[00:23:04] it is important that you go through the courses

[00:23:06] because today's date

[00:23:08] if I say that I have trained people

[00:23:10] I don't have trained people

[00:23:12] I am training people

[00:23:14] I am bringing those kind of trainings

[00:23:16] to my entire team

[00:23:18] wherein they can upskill themselves on those things

[00:23:20] So, if they want to be ready

[00:23:22] then I will say that Python is a good start

[00:23:24] but it is not sufficient

[00:23:26] because you don't know

[00:23:28] which library is the right library

[00:23:30] for JNAI

[00:23:32] or any other such emerging tech

[00:23:34] So, because in today's date

[00:23:36] speech to text is a very common thing

[00:23:38] chatbots are very common thing

[00:23:40] but it has not become common yet

[00:23:42] we are just looking at it

[00:23:44] and I think that in those places

[00:23:46] where we need to go to an ICCI banking site

[00:23:48] I feel that chatbot is strange

[00:23:50] But it is not necessary

[00:23:52] that they are providing the right solution

[00:23:54] they are still learning

[00:23:56] See, they are not going to replace

[00:23:58] first of all the people

[00:24:00] they are not going to replace

[00:24:02] they are going to stay here

[00:24:04] they are going to help us

[00:24:06] in the way we deliver our work

[00:24:08] it was very different 15 years back

[00:24:10] versus what it is right now

[00:24:12] we need to be skilled so that we can use them well

[00:24:14] because as you said

[00:24:16] they are not accurate

[00:24:18] So, as it is

[00:24:20] any customer who can't get out of it

[00:24:22] will have to validate it

[00:24:24] So, it's important that you learn with it

[00:24:26] for this

[00:24:28] what I do is

[00:24:30] I attend conferences

[00:24:32] I personally experiment with the different techniques

[00:24:34] get hands on knowledge

[00:24:36] so that I can share

[00:24:38] impart that knowledge to my team members

[00:24:40] So, I would say

[00:24:42] you need to watch out for those things

[00:24:44] you need to learn

[00:24:46] and then guide your team members as well

[00:24:48] Have that vision

[00:24:50] That's important

[00:24:52] Thank you

[00:24:53] One last question before we close this entire serious conversation

[00:24:56] You also hire people

[00:24:58] When you hire people

[00:25:00] what exactly you look into them

[00:25:02] that these are the skills

[00:25:04] which I want

[00:25:06] so if you throw some light

[00:25:09] the people who are looking into this role

[00:25:11] that they know that ok

[00:25:13] you have to prepare for the interview

[00:25:15] So, aptitude and attitude

[00:25:17] if you have these two things

[00:25:19] then you are an awesome mix

[00:25:21] of the right skills

[00:25:23] and the right trade

[00:25:25] which is needed for the job

[00:25:27] and I am happy to hire you

[00:25:29] honestly

[00:25:31] so if you don't have the right attitude

[00:25:33] then no matter how aptitude you are

[00:25:35] you can't learn the attitude

[00:25:37] because on the job people require you to deliver

[00:25:40] so for that integrity

[00:25:42] so when I say attitude it's the integrity

[00:25:44] you need to be true to yourself

[00:25:46] you need to be true to your leader

[00:25:48] if you don't know you don't know

[00:25:50] you can't come and hide that

[00:25:52] ok, I will escape

[00:25:54] I won't let the leader know

[00:25:56] this is not the case

[00:25:58] the leader knows everything

[00:26:00] so it's important that you carry these two things

[00:26:02] now if I go into the specific details

[00:26:04] of when I say aptitude and attitude

[00:26:06] your aptitude is the problem solving skills

[00:26:08] they are the most critical skills

[00:26:10] a person needs to have

[00:26:12] I have talked about all technical skills

[00:26:14] like python, tably

[00:26:16] all those are add-on

[00:26:18] the basic skill is problem solving skill

[00:26:20] how you structure the problem

[00:26:22] how you define the problem

[00:26:24] and how you solve it

[00:26:26] if you know this

[00:26:28] so there are two kinds of hiring

[00:26:30] one is campus hiring

[00:26:32] and the other is lateral hiring

[00:26:34] we know they don't expect skills

[00:26:36] they are to carry skills

[00:26:38] they don't know python

[00:26:40] they don't know tably

[00:26:42] maybe someone knows

[00:26:44] so we are shortlisted

[00:26:46] but otherwise

[00:26:48] at that time what we focus is

[00:26:50] problem solving skills

[00:26:52] we ask them questions

[00:26:54] we ask them number problems

[00:26:56] we give them some case studies

[00:26:58] something like guesstimates

[00:27:00] so we are not looking for answers

[00:27:02] we are looking for the structure

[00:27:04] how that person is approaching the problem

[00:27:06] and we focus on

[00:27:08] team work

[00:27:10] team management skills

[00:27:12] time management skills

[00:27:14] conflict management

[00:27:16] because every person has

[00:27:18] an initiative

[00:27:20] or something in school

[00:27:22] at least they can talk about those examples

[00:27:24] so if you have this as a campus hire

[00:27:26] we are actively hiring those people

[00:27:28] actively

[00:27:31] and on the lateral side if I were to cover

[00:27:33] there again we

[00:27:35] definitely look into these skills which I covered

[00:27:37] for the campus hire

[00:27:39] but on top of that

[00:27:41] when you are coming as a lateral hire

[00:27:43] we expect you to carry the skills

[00:27:45] personally if I were to say

[00:27:47] I don't look into the background of somebody

[00:27:49] I don't see that he has come from IIT

[00:27:51] or from a normal college

[00:27:53] I don't get that much of a difference

[00:27:55] what matters to me

[00:27:57] is the skill you mentioned

[00:27:59] you are able to prove your metal

[00:28:01] and if you are great on that skill

[00:28:03] most welcome to my team

[00:28:05] so that's how I hire

[00:28:07] and I have had great success with those people

[00:28:09] that's my personal experience

[00:28:11] of hiring so many people

[00:28:13] and having them along with me

[00:28:15] and they are not necessarily the ITIM people

[00:28:17] it can be anybody

[00:28:19] great thanks Anita

[00:28:21] thanks for sharing that experiences with us

[00:28:23] so here we will close this section

[00:28:25] and we will move into another section

[00:28:27] which is unwind

[00:28:28] so we will not talk about data analytics

[00:28:30] we will talk about more of you

[00:28:32] you have a data powerhouse right

[00:28:34] how do you unwind yourself from work

[00:28:36] I would say that

[00:28:38] during the work itself

[00:28:40] I enjoy a lot

[00:28:42] personally if you ask

[00:28:44] any of my team member

[00:28:46] or anybody on the floor

[00:28:48] they will say

[00:28:50] probably I am overhiving

[00:28:52] but it's fun

[00:28:54] because when I am speaking to people

[00:28:56] I don't feel like I am just talking about work

[00:28:58] so honestly

[00:29:00] if you love your job

[00:29:02] you don't feel pressure

[00:29:04] so specifically I didn't have to unwind

[00:29:06] but if

[00:29:08] if I were to do it

[00:29:10] then Google and GEO

[00:29:12] are there for me

[00:29:14] and they are smiling

[00:29:16] and they are care for me

[00:29:18] that helps me

[00:29:20] that's a stress buster for me

[00:29:22] so I spend time with them

[00:29:24] success is something

[00:29:26] which makes you happy

[00:29:28] which makes you accomplished

[00:29:32] from where I come

[00:29:34] the way I mean

[00:29:36] the story we heard

[00:29:38] I didn't bring any silver spoon

[00:29:40] it's clear

[00:29:42] I come from a background

[00:29:44] where there was a conservative environment

[00:29:46] but we were very much satisfied

[00:29:48] whatever my dad

[00:29:50] or brother were earning

[00:29:53] but there

[00:29:55] I would say we didn't have a car

[00:29:57] if I was born or grown up

[00:29:59] when I bought my first car

[00:30:01] in my job

[00:30:03] so that was a huge success

[00:30:05] so over the time

[00:30:07] the things I achieved

[00:30:09] they were added to my success

[00:30:11] I feel I am successful

[00:30:13] because I am very much content

[00:30:15] I feel that God has given me

[00:30:17] plus

[00:30:19] I think it is important to be satisfied

[00:30:21] when you are acknowledged

[00:30:23] someone acknowledges you are doing a good job

[00:30:25] and I think the kind of organizations

[00:30:27] I have worked with

[00:30:29] I am fortunate that my leaders acknowledge me a lot

[00:30:31] they make me feel important

[00:30:33] in the organization

[00:30:35] my inputs are valuable

[00:30:37] I have been given

[00:30:39] different kind of challenging projects

[00:30:41] opportunities

[00:30:43] which have made me feel

[00:30:45] that I am important to this organization

[00:30:47] I am important to my leader

[00:30:49] that is how I define my success

[00:30:51] because that gives me happiness

[00:30:53] so I think that's pretty much

[00:30:56] you are studying a lot

[00:30:58] yes, I am still studying

[00:31:00] that's a fact

[00:31:02] so are you still into reading?

[00:31:04] I am still into reading

[00:31:06] I am more into guided learning

[00:31:10] than anything else

[00:31:12] I feel that I should pursue courses

[00:31:14] that helps a lot

[00:31:16] because it has been the same since the beginning

[00:31:18] I have done BSc, MSc, MTech

[00:31:20] then I have done CFA

[00:31:22] level 1

[00:31:24] then I started doing BSc

[00:31:26] from Chennai

[00:31:28] in data science

[00:31:30] I have completed

[00:31:32] the 2.5 year in that

[00:31:34] I have taken the diploma of data science

[00:31:36] so I feel that guided learning

[00:31:38] because there is so much material on the internet

[00:31:40] it becomes very difficult

[00:31:42] otherwise you have to absorb

[00:31:44] so I am a little bit like that

[00:31:46] that I am going through a lot of things

[00:31:48] so that's how I believe

[00:31:50] okay

[00:31:52] thanks Anita, thanks for sharing

[00:31:54] your heart out with us

[00:31:56] it's great talking to you

[00:31:58] and thank you so much Abhishek

[00:32:00] it's been a pleasure talking to you

[00:32:02] as you said that it was very heavy

[00:32:04] I did not feel like

[00:32:06] I felt like we have given an interview

[00:32:08] so thank you so much

[00:32:10] so this was our episode with Anita

[00:32:12] on career in data analytics

[00:32:15] so much about learning

[00:32:17] and how to keep up yourself

[00:32:19] by learning new things

[00:32:21] we have talked about so many hard skills

[00:32:23] which we need to work on

[00:32:25] along with soft skills

[00:32:27] which are absolutely

[00:32:29] which will make you a complete professional

[00:32:31] so don't just focus on hard skills

[00:32:33] focus on soft skills too

[00:32:35] and as Anita talked about

[00:32:37] that Python

[00:32:39] data visualization tools

[00:32:41] there is one more tool

[00:32:43] we will bring you more

[00:32:45] such conversations in the future

[00:32:47] so do share your feedback

[00:32:49] so that we can pick up the right topics for you

[00:32:51] and also if you are watching it

[00:32:53] on YouTube

[00:32:55] like, share and subscribe

[00:32:57] and do share it with your colleagues as well

[00:32:59] and if you are listening to us

[00:33:01] on audio podcast channels

[00:33:03] do follow us

[00:33:05] so for a time being

[00:33:07] we are signing off from now

[00:33:09] this is Abhishek Mittal