Beena is a managing director with Deloitte Consulting LLP and is an award-winning senior executive with extensive global experience in artificial intelligence and digital transformation. Her knowledge spans across e-commerce, financial, marketing, telecom, retail, software products, services, and industrial domains with companies such as HPE, GE, Thomson Reuters, British Telecom, Bank of America, E*TRADE and a number of Silicon Valley startups. Beena is the founder and CEO of Humans For AI Inc. She has co-authored the book “AI Transforming Business”.
A well-recognized thought leader and keynote speaker in the industry, Beena also serves on the industrial advisory board at Cal Poly College of Engineering, and she has been a board member and advisor to several startups including Flerish, Predii, iguazio, CliniVantage, and ProjectileX.
Beena has been honored several times for her contribution to tech and her philanthropic efforts, including: UC Berkeley 2018 Woman of the Year in Business Analytics, San Francisco Business Times’ 2017 Most Influential Women in Bay Area, WITI’s Women in Technology Hall of Fame, National Diversity Council’s Top 50 Multicultural Leaders in Tech, CIO.com and Drexel University’s Analytics 50 innovator, Forbes Top 8 Female Analytics Experts, and Women Super Achiever Award from World Women’s Leadership Congress.
Beena thrives on envisioning and architecting how data, artificial intelligence and technology in general, can make our world a better, easier place to live.
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“ My bet is that computer programming, the way we know it today, is going to fundamentally change.”
In this episode we’ll also cover:
- Why Statistics and Algebra are fundamental for data scientists
- How Beena got involved in managing people after starting on a very technical path.
- What made Beena start a technology nonprofit?
4:30 – Why Statistics and Algebra are fundamental math classes for data scientists
9:57 – Beena explains why she has worked across many roles across a variety of industries. (Hint: its curiosity)
13:44 – How Beena got involved in managing people after starting on a very technical path.
18:37 – Beena recalls how taking a job as a data architect changed her career path and sparked her interest in AI
Grant Ingersoll: 00:19 Welcome everyone to the development or podcast, your source for interviews and content on careers and technology. I’m your host grant Ingersoll. Each episode we try to highlight interesting people working in a variety of roles in tech with the goal of helping our listeners find the right role for them, whether that’s in software development, product management, sales, marketing, or really any other role that goes into building a technology focused company.
To that end, today’s guest has a variety of deeply tie, has had it variety of deeply technical roles as an engineer and data scientist before she moved up through the ladder and into the ranks of technical leadership. She in that time has also worked on several boards, both as a member of the board and an advisor and as if that isn’t enough, she’s also started a nonprofit focused on inclusivity in the data science and artificial intelligence space. Please welcome to the development or podcast Beena Ammanath. Beena, great to have you here.
Beena Ammanath: 01:18 Thank you grant. Pleasure to be here.
Grant Ingersoll: 01:20 Yeah, and thanks so much for joining me again. I know you have a very busy schedule and, and you know, as I was doing my research on you, it’s difficult to kind of do you justice in terms of all the amazing things you’ve done in your career. So how about we just start off by having you introduce yourself and, and walk us through that career a little bit.
Beena Ammanath: 01:38 Yeah, absolutely. And I think you did a great job with the introduction. So thank you for all the glowing wprds.
Grant Ingersoll: 01:45 You’re welcome.
Beena Ammanath: 01:46 Now I have a very traditional bachelor’s and master’s degree in computer science, so studied computer science and loved it. And then I have literally grown through the ranks right from being a DBA to a data analyst, to a SQL developer and leading a day a team of BI engineers, data engineers, leading a team of data scientists. And what has helped me which I certainly didn’t plan it that way, has been, I have been always anchored in data but work across different industries, whether it’s telecom or manufacturing or finance or retail.
It’s been different industry verticals, but anchored in data and data is such a good space to be in, right? Because data that space has just, you know evolved from a traditional sequel and sequel databases to oil TP to databases to that from that to business intelligence and ETL, that whole space and then came Hadoop and big data and then machine learning and data science. And now it’s all about AI. But at the end of the day, the foundation is data. So I think that has been the theme with my career.
Grant Ingersoll: 03:03 Yeah. So yeah. Well I wanted to unpack that a little bit more between, cause you know, very early on, I think you mentioned you did a lot of what would a lot of just programming and development, you know, software engineering and then there was this switch to, Oh, Hey there’s this, there’s this data stuff as well. What was the catalyst at the time, you know, as you were kind of analyzing your career choices early on there, like what did you look and say, Oh, I’m going to switch more and be more data focused then more let’s say programming centric?
Beena Ammanath: 03:36 It’s interesting that you ask that, right? I mean, it was software engineering initially, but it was really around you know, used to you had to write these long SQL scripts. So it was still very closely tied to the database side of things. I’ve always found myself attracted towards the, the data aspect because you just, I just feel that you can learn so much by looking at data and what the reason that it attracts me besides being, you know, more attractive and insightful is I feel it’s more mathematical compared to software engineering, at least that you’re at that when I was doing hands on programming, it was syntax and things like that was so crucial that you couldn’t really analyze as quickly or do get results quickly the way you would do on the data side.
Grant Ingersoll: 04:31 Mm. Well, so on the, you know, let’s, let’s delve into that math side a little bit. Cause you know, traditional computer science, isn’t that math heavy? Was this just, was this something that you always enjoyed on your own and you’re like, Oh Hey, wow, I can go do math on this in the computer realm? Or was math something that you took up a bit later as you, as you dug in on the data side of things?
Beena Ammanath: 04:55 Yeah, that, that, that’s, that’s a great question. So I have naturally been good at math. I mean, even when I was studying at night from elementary school, math just came naturally to me, but I didn’t like it per se. My favorite subject was history and you know, math was just easy. And once I started, you know, got into this field and we did have a few courses around statistics and but it just seems that my natural inclination is towards math, though I refuse to still accepted fully that, you know, I enjoy math. I don’t think I enjoy math. I just have a natural inclination, natural talent for it.
Grant Ingersoll: 05:38 Yeah. No, that’s interesting. The notion of enjoying math is a, is to a lot of people, a little bit of an absurd notion. I can understand that. I actually for a long time enjoyed math and then it became work as well. So there’s, I think you go through this evolution of math well talk a little bit about some of the math you do because you know, we’re often in data science talking things like statistics and probabilities and that kind of math as opposed to more theoretical. Is that, is that a fair statement of, of how you would view the math you do in your career?
Beena Ammanath: 06:16 Yeah, absolutely. I don’t, you know, I think that the applications of math is different if you’re an economist or you know, or a hedge fund manager, it’s a different type of math that have, you know, the of math you use as a data scientist or in the tech industry is different. But I, I tell this to a lot of my mentees is, you know, you need to get your foundation right. You need to know that the foundational concepts of, you know, not just math but physics and chemistry. You need to know the foundation and then you can apply it in different scenarios. But we do tend to apply more statistics and fundamentally being that for even from the business intelligence and now machine learning space, lot of it is focused on that application of math.
Grant Ingersoll: 07:03 Yeah. What would you see as some of those key foundations? You know, like if if the listener out there is like, Hey, I need to, I’m going to go take let’s say two math classes, what would you recommend they take if they want to get into the data science space?
Beena Ammanath: 07:19 Statistics for sure. And core algebra, geometry, eh, you know though those are core concepts. Yeah. Especially algebra and statistics. I think those are the two that we tend to use repetitively. And I think the ones you study that in school, I remember it as in high school and you were studying that as like why, why, when am I ever going to use it? And then you’re applying it like 20 years later because it is so ingrained in your brain by that point that we are using it. But when you’re in school, you’re not sure whether you would ever use those trigonometric formula in your life, but always does come in.
Grant Ingersoll: 08:02 Well, and the beauty these days is like, it’s important to know and understand them, but you often have the computer to help you with that math too. I mean, very rarely are you writing your own math from scratch. Right,
Beena Ammanath: 08:15 Right. And you know, iLab spinning off a little bit on that, I think you know, my, my bet is that computer programming, the way we know it today is going to fundamentally change. It has to, I think programming or coding, the way we do it today is not going to be around for in the next 15 years. It’s where programming is going to be completely different and it’s going to be more human-friendly. It’s not just going to be about writing the actual code, the in a structured manner, I think that is set to go away.
Grant Ingersoll: 08:54 Yeah. You’re seeing these, you know, the, the, the Four GL capabilities are getting better and better. And then you know, I think in th we’ll touch on this later with your foundation, but I think the, you know, this notion that all of this next generation of developers will be AI native, right? They will just have this function available to them that says, make this program smarter over time.
I think you’re dead on. And your, your analysis there, you know, for the next question, I want to kind of go back to something you already touched on here and that you’ve worked across a lot of different industries, you know, telecom, finance, it looks like, I believe you’ve done some consumer facing applications as well, you know, talk about kind of some of the things in your career that have helped you be successful across all of those different industries.
Grant Ingersoll: 09:46 Cause I think a lot of people will say, Oh, well, I’m going to go into this thing and that’s the thing I have to do. I’m going to be in telecom because that’s my expertise. But you’ve pretty successfully jumped across.
Beena Ammanath: 09:57 Yes. and the reason was you know, I’m a very curious person and I love learning. You know, and it’s always about what peaks my interest. So every role change or job change that I’ve had is when I have kind of become very comfortable in that role. It’s been like a, it’s become you, you get into your comfort zone, right? You know, everybody at the job, you know how to do it very well and that’s, that’s a trigger for me to go and learn something new. The way that I’ve approached is, you know, and sort of trying to learn something new from, you know, brand new from a technology perspective, which is any way force is try and learn, learn about a new domain.
Beena Ammanath: 10:39 Like for example, when I joined GE, I had, I have no background in manufacturing. I did not have any background in IOT. But to join a company like GE was, it was a huge learning, right? You learn so much more about the domain by working in a company which is deeply anchored in them. I’ve certainly seen them careers where somebody out of college where you join a job and you are added for the next 50 years. But what I’ve seen in the the really successful people are the ones who have career mosaic is what I call it, right? Have where you have different parts. You know, you have different experiences and somehow they all come together beautifully and enriches the whole experience. So, so that you’re bringing so much more to the table than if you were just in one specific industry or one specific role.
You get really, really good at it, but you know, you don’t really, are not able to try in another environment. And I think in the new economy it’s going to be people who have career mosaics that. And it’s, it could be anything across domains or across functions. I think that always keeps you as a person mentally on your toes and you’re always learning. It’s it’s, it gives that motivation to, for you to push yourself without any exponents.
Grant Ingersoll: 12:08 Yeah. That’s such a beautiful turn of phrase right there. Career mosaic. I’ve never thought of it that way. But I think that’s, that’s such a nice way of thinking about and about building a body of work that is your, your career. So speaking of that. I mean, I imagine then some of this, these different experiences then have also been a really key part for you moving up the ranks from individual contributor to leader, you know, talk about some of the key shifts that took place that enabled you to make that leap from a individual contributor to a leader.
Beena Ammanath: 12:48 Yes. so that actually was a natural evolution in terms of growing you know, you start as an individual contributor and as you grow there you know, more lesser experience the more junior level employees who join and then you kind of become a mentor and then becomes more formal and manager.
But at one point, you know I did have to and this was a discussion with the of the organization at that time and also with within myself to say, do I want to go down a pure technical career path, which is a much like, you know, you’ll become a chief architect or a technical fellow. These are terms in the industry, which is like you have reached the highest rank within a technical and it’s, it’s as good as a, you know, as a manager level, right? So go down a people management path.
Beena Ammanath: 13:44 And I tend to, I chose a people management fad because you know, I, I like technology, but I wouldn’t say I’m fascinated by technology for the sake of technology. I, I like to you know, the see how technology can actually be used either to get new business outcomes or to improve productivity or to do something with the technology. So I you know, I haven’t shared this with before, but after my master’s degree, I did consider doing a PhD and doing more research and I did, you know, take on that.
And that’s when I realized my strength lies at the intersection of the application of technology and core technology. That was a big motivator for me to go down the people management path. Obviously the other factor being I enjoy working with people. I do tend to be naturally naturally affiliated with humans and people and to have that I truly enjoy enjoy relationships with people.
Grant Ingersoll: 14:57 Yeah. So that’s actually, that’s always an interesting one when you’re talking with technologists because I think a lot of people, you know, I know personally in my own early career, I was like, Oh, just tech, tech tech and all I wanted to do is write code and people were secondary. But then as you grow and you, you experience more things, you go across industries, this people’s side comes out and you know, you and I have met in person and you’re very personable.
And, and so I guess what you’re also saying is that, that that was a natural fit for you. I was wondering if there was any specific things on people management that you really had to kind of open up that learning curve again or, or open up that open up your mind to how you properly manage people? Like what are some of those key lessons and being a successful people manager?
Beena Ammanath: 15:48 I think in the new era being able to like no matter what your role is, being able to scale up and scale down rapidly as a people manager is really important. And what I mean by that, as a people leader, you need to be able to talk at an executive level with your stakeholders, but at the same time you need to be able to talk to your engineers at a code level.
That’s where I’ve seen the most you know respect coming from both your employees and your, your own leadership, right, is when you are able to be able to quickly go to whichever level you need to. For me personally, it worked out very well. Again, another one of those unplanned things. But I was leading a fairly large team at a senior management level when I had my first child and wanting to be a super mom and you know, be actively engaged.
Beena Ammanath: 16:51 I took a step back in my career and took, took on an architect role, which was an icy. So I went from a people manager to an individual contributor and, and if, you know, coincidentally or a, you know, the stars were aligned. It was around the time when Hadoop and big data was just coming into the picture. And I tell you, grant thinks that what I studied in computer science, there was no Hadoop, there was no big data concept in that there was AI, but there was no big data that was not a thing.
And it’s a very different style of programming. The programming languages that I studied with like DBAs and Pascual and FoxPro notice and things that are not, you know, don’t even exist. So why that gave me the opportunity was that to get literal very much more hands on and learn these technology contructs, which otherwise if I had gone down that traditional path of just growing, continuing to grow from a management perspective would have been very hard.
Beena Ammanath: 17:58 So I did that for five years and then, you know, I got back into the management career path again once my son was a bit older. What enabled me was really, you know, being able to understand the new technology and having a little bit more grasp, which I wouldn’t have had otherwise. So from my, you know, what I see to, you know, the leaders who report to me is you need to be on top of technology. This space is evolving so fast. You cannot run an engineering team anymore unless, and until you have your technical chops up to date.
Grant Ingersoll: 18:37 Yeah, no, there’s, wow, there’s, there’s so much rich stuff right in there. I mean you’ve, you’ve had, you know, I love this idea and people’s careers of like these serendipitous moments where, you know, PR, you know, obviously you’re super excited about having your first child at the same time. There’s this part of you on the career side that it’s like, Oh, well, Hey, I’m, I’m taking a step back from what I thought I was going to do.
But then, you know, now in hindsight, all the, you know, these years later, you’re like, yeah, but that was actually really worked out well for me. And, and I think there’s a lot of those being a math geek myself, I like to call them, you know, their inflection points. Right. And it sounds like, you know, you, you had some really nice things come out of that inflection point back in your career.
Beena Ammanath: 19:25 Yes, absolutely. Are you even studying AI right? Who knew it would be, it would be so prevalent in our own life, you know, at that time, even post realized marketing was considered impossible. Automating personalized marketing was also built in the 80s
Grant Ingersoll: 19:43 Yeah. Well, and the beauty of AI right, is your foundation is statistics. And, and data programming back in the day. I mean, you know, don’t tell all the marketing people, but right. Qai is just, you know, really smart counting at the end of the day.
Beena Ammanath: 20:01 Yes, exactly. Exactly.
Grant Ingersoll: 20:04 Let’s talk about the leadership role a little bit because I love when I have people like you on as guests, you know, who have managed large teams and, and you know, to flip the tables a little bit just from your, your career path to how do you think about hiring and building a team and mentoring people in their career path because that’s often a really important role in leadership.
Beena Ammanath: 20:27 Oh, yes, absolutely. And I’ve worked in some tough companies which and some you know, which don’t necessarily attract the best software talent or you know, data science talent, which is really hard to hire and, and you have to get a, you know, fundamentally understand the kind of talent you’re hiring.
And one thing that you know, I learned is people today are not employees today are not necessarily looking to join a company. It might be the first move, but they, and you are interviewing someone, they’re interviewing you as well, and they looking for inspiring leaders. Mmm. And they are looking for somebody whom they can follow and somebody whom they can respect. And for me, you know, that meant some changes. Did I tell a few years ago I was, and we’ve met at an event, I would not, I did not speak publicly.
Beena Ammanath: 21:26 I did not blog or write or do things which I do as much today, which was really to kind of attract that talent to, to build out that network. Today we live in a space of ecosystems and networks and you know, as leaders, we have to tap into these new or newer spaces, which was not there. You know, I don’t think 20 years ago anybody was looking at LinkedIn or any kind of social media to attract talent. But today that’s what the talent today does. They do their research, they will look for leaders who they can follow or who are inspiring.
So for me that that has helped, but also being able to, I remember, you know, I can, I bet I can still do this is go around team of data scientists and if they’re coding, look at their code really quickly and find out little things, right? I started, I did, I’ll be honest, grant, I don’t think I can roll up my sleeves and code the way I could even 10 years ago, 15 years ago. But, but I can certainly, you know, I know the foundational aspects, right? So I can sort of, and that brings out the respect and they like, Oh, how do you know this? You are a VP, you’re not supposed to know it too. And then Zen network that comes into, so it, all, you know, it all works out at that network level.
Grant Ingersoll: 22:53 Yeah. Well, and, and you know, and along these lines, you know, you’re super busy as a, as a leader in a large global organization. Then you also are very generous with your time. And you know, I noticed on your profile, you have a number of board roles. Of course you’ve also started this nonprofit. So let’s shift gears a little bit and talk about that side of of being in a leadership role of, of taking on these advisory things of starting to contribute more broadly. Like how did you, how did you get your start on that, that side of the equation?
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