Latest / Chief in Tech Deep Dive Shortcast / Chief in Tech S1E47 - Hindoli Roy : From Loud to Legendary – My Story of Reinventing Inclusive Leadership

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Chief in Tech S1E47 - Hindoli Roy : From Loud to Legendary – My Story of Reinventing Inclusive Leadership · Chief in Tech Deep Dive Shortcast

• 0:00
  I want you to imagine just for a second that
• 0:02
  you are sitting in one of those incredibly high
• 0:05
  stakes corporate meetings. Oh, yeah. We've all
• 0:08
  been there. Right. Like, you know the exact environment.
• 0:12
  The air conditioning is always freezing. The
• 0:14
  tension is just thick in the room. And everyone
• 0:16
  is staring at this complicated dashboard that
• 0:18
  isn't really making any sense. And no one wants
• 0:20
  to admit they don't understand it. Exactly. But
• 0:24
  you, out of everyone sitting around that massive
• 0:26
  table, you actually have the solution. you see
• 0:29
  the exact bottleneck. So you gear up to say something.
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  Yeah, so you take a breath, you lean forward,
• 0:35
  you speak up, and you lay out your idea. And
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  the moment you finish, instead of the room acknowledging
• 0:40
  the solution, the conversation just awkwardly
• 0:43
  moves on. Yeah, the classic brush off. Or, I
• 0:46
  mean, perhaps even worse, you get pulled aside
• 0:48
  by a manager afterward. And you're explicitly
• 0:50
  told that you were coming across as, quote, too
• 0:53
  loud. Ugh, it is just an incredibly deflating
• 0:56
  experience. It really is. Because you bring your
• 0:59
  passion and your technical expertise to the table,
• 1:02
  and the feedback you receive has absolutely nothing
• 1:05
  to do with the quality of your idea. Right, nothing
• 1:07
  about the actual merit. Exactly. The critique
• 1:10
  is entirely about your delivery. And it is usually
• 1:13
  aimed at just making you take up less space in
• 1:16
  the room. Well, that specific scenario is the
• 1:18
  jumping off point for our deep dive today. We're
• 1:21
  looking at a really compelling address titled
• 1:23
  The Architect of Inclusion Leading with Voice
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  and Vision. Delivered at the Women in Tech Global
• 1:28
  Conference in 2025. Yes, exactly. By a tech leader
• 1:32
  named Hindoli. And it serves as a fascinating
• 1:35
  piece of source material because she isn't operating
• 1:39
  in the realm of abstract leadership theory here.
• 1:41
  No, not at all. She is giving us a strategic
• 1:43
  breakdown straight from the trenches of the tech
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  industry based on real lived experiences. Right.
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  And our mission for this deep dive is to basically
• 1:51
  extract the actionable mechanisms from her 15
• 1:55
  -year. It's quite a journey to trace. It really
• 1:59
  is. We are tracing a journey that starts with
• 2:01
  a software engineer fighting just to get her
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  voice heard, and it ends with a VP who actively
• 2:07
  shapes the culture of her entire domain. Yeah.
• 2:10
  Okay, let's unpack this, because before we can
• 2:13
  really analyze the strategy she used to overcome
• 2:15
  those workplace barriers, we have to look at
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  the raw materials she was working with. Her background
• 2:20
  is definitely unique. Yeah, it's this unique
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  blend of skills, and that blend directly informs
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  how she eventually learned to deploy her voice.
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  Well, if you want to understand someone's operational
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  style, you have to look at their foundation first.
• 2:31
  Right. Her 15 -year journey begins with a Roops
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  in India, starting out as a software engineer.
• 2:38
  And over that time, she worked globally. for
• 2:41
  massive, incredibly complex organizations. Like
• 2:44
  Accenture, Deloitte, and JP Morgan. Heavy hitters.
• 2:47
  Oh, absolutely. And her technical breadth is
• 2:49
  just constantly expanding through all of this.
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  Yeah, the text notes that she worked on traditional
• 2:54
  AI, then moved into mobile applications, and
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  eventually landed in her current role in the
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  FanOps domain. Managing really large -scale products.
• 3:01
  Right. But hold on. Let me make sure I'm understanding
• 3:03
  this progression, because tech moves so fast.
• 3:06
  When the source mentions traditional AI, what
• 3:09
  does that actually mean in the context of her
• 3:12
  early career? Like, compared to the generative
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  AI models we see today, it's a really vital distinction.
• 3:19
  Traditional AI, in the context of her earlier
• 3:21
  engineering days, refers to highly structured,
• 3:25
  rule -based machine learning. OK. So more about
• 3:27
  numbers than generating text. Exactly. Think
• 3:30
  of predictive algorithms. It's about taking massive
• 3:33
  data sets, finding patterns, and making statistical
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  predictions. Like forecasting user behavior or
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  maybe detecting fraud? Spot on. And doing that
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  requires a rigid, highly mathematical mindset.
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  You are building models with very, very strict
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  parameters. OK. And from those rigid models,
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  she eventually transitions into phenops. Right.
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  Just so we don't leave anyone behind, because
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  I hear that term thrown around a lot lately,
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  what does the intersection of finance and DevOps
• 3:59
  actually look like in a day -to -day corporate
• 4:01
  environment? Well, let's break it down. DevOps
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  is the practice of merging software development
• 4:07
  with IT operations. Right. So a company can continuously
• 4:11
  update and deploy applications quickly. Yes.
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  But when a massive company like JPMorgan moves
• 4:16
  all that rapid development to the cloud, the
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  costs can just spiral out of control instantly
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  if you aren't paying close attention. Oh, I can
• 4:23
  imagine. Yeah. Like someone leaves a massive
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  server running over the weekend, and suddenly
• 4:27
  you burn through $100 ,000. Wow. So Phenops is
• 4:30
  the leash on that. Exactly. Phenops brings financial
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  accountability to that cloud spend. You are optimizing
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  massive scale products in real time. It sounds
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  stressful. It is a rigorous, deeply analytical
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  domain. I mean, a tiny coding error literally
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  translates to a massive financial loss. Which
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  makes the next detail in her address stand out
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  so starkly to me because alongside all of this
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  intense, analytical, high -stakes engineering
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  work, she is deeply passionate about dance and
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  creativity. It's such a striking contrast. Yeah.
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  She explicitly mentions that she wrote this very
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  address for the tech conference while she was
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  actively preparing for a dance performance in
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  Greece. You have the highly structured world
• 5:15
  of managing cloud computing costs on one side,
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  right? Yeah. And then the fluid expressive world
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  of a stage performance completely on the other.
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  It honestly makes me view her entire career trajectory
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  through the lens of choreography. Oh, how so?
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  Well, to succeed in something as technically
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  unforgiving as fine ops or to succeed as a high
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  -level dancer, you absolutely must have a foundation
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  of rigid technical precision. Right. You have
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  to know the code, the math. Exactly. Or the physical
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  steps in dance. But to truly stand out and lead,
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  to become a VP or a lead performer, you need
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  to create a flexibility to express yourself.
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  To read the environment. Yeah. And to adapt to
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  the music or, in her case, adapt to the market
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  and the executive team. What's fascinating here
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  is that we see this pattern consistently among
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  the most innovative leaders in highly technical
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  fields. Really? Like it's a common thread? Oh,
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  for sure. The executives who can truly problem
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  solve at a VP level rarely draw their inspiration
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  solely from the technical manuals of their industry.
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  That makes total sense. They need outside inspiration.
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  Right. The spatial awareness, the rhythm, and
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  the emotional intelligence required for dance,
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  those are the exact same abstract mechanisms
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  you need to manage a large -scale product team.
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  Because you're dealing with people, not just
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  machines. Exactly. You have to know when to push
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  an initiative, when to yield, and how all the
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  different moving parts of your organization harmonize.
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  So that really paints a picture of a vibrant
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  you know, multi -dimensional professional. But
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  that vibrant background is exactly what crashed
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  directly into a very rigid corporate wall early
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  in her career. It did. The passion that fueled
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  her creativity and made her a driven engineer
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  became a sudden liability when she stepped into
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  male -dominated meeting rooms. Yeah, and she
• 7:02
  details this early struggle very transparently.
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  I mean, she had the technical foundation from
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  places like Deloitte. She had strong ideas for
• 7:08
  optimizing projects, but she felt constantly
• 7:10
  shut down. Overlooked, yeah. And then came...
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  the reprimand. She was formally told that her
• 7:15
  communication style was too loud. Which is just
• 7:18
  devastating to hear. The text notes this caused
• 7:20
  intense self -doubt, making her question how
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  she operated at a fundamental level. But I have
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  to push back on the corporate logic operating
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  in that room for a second. Okay, lay it on me.
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  Wait, in a competitive meeting at a major financial
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  institution, aren't you supposed to speak up?
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  You would certainly think so. Right. If you are
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  trying to convince a room of skeptics to change
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  a product roadmap, are you supposed to project
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  confidence, assert your ideas, and essentially
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  be loud enough to break through the noise? Why
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  is volume suddenly treated as a penalty? This
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  raises an important question about the hidden
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  mechanics of workplace dynamics. Why is the exact
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  same assertive behavior rewarded in one demographic
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  and penalized in another? Yeah, why the discrepancy?
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  Well, Hindley points directly to the answer in
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  her address. Unconscious bias. Ah, the invisible
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  filter in the room. Yes, unconscious bias fundamentally
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  skews how assertiveness is decoded by an audience.
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  And the source material outlines this double
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  standard pretty clearly, right? It really does.
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  When a man in a highly technical meeting speaks
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  forcefully and passionately, the unconscious
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  bias of the listeners often automatically codes
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  that behavior as leadership. Like, oh, he's a
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  real go -getter. Exactly. It's perceived as taking
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  charge of the situation. But when a woman, particularly
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  a woman of color in a male dominated engineering
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  space, exhibits that exact same volume and passion.
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  It's seen differently. Completely differently.
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  The unconscious bias codes it as aggressive or
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  disruptive. The message is lost. because the
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  audience is entirely focused on policing the
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  delivery. Man, if the rules of engagement are
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  completely different depending on who is speaking,
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  that could easily cause someone to just shut
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  down entirely. Oh, without a doubt. I mean, if
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  the system is wired to reject your frequency,
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  why bother broadcasting at all? And that is the
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  exact trap of self -doubt she found herself in.
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  She was seriously considering shrinking her presence.
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  Just blending into the background. Right. But
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  she received a piece of advice from a mentor
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  that shifted her entire perspective. Oh. What
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  did they say? The mentor told her she didn't
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  need to change her volume. And she didn't need
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  to lose her passion. She needed to rewire her
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  communication approach. OK, I love that phrasing.
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  You don't throw out the machine. You just change
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  how the circuits connect. Exactly. Once she understood
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  that the game was complicated by this invisible
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  layer of unconscious bias, she stopped trying
• 9:47
  to just push harder against a locked door. She
• 9:51
  got strategic. Yeah. Instead, she developed specific
• 9:54
  strategies to navigate the room. And the first
• 9:56
  step in that rewiring process was learning the
• 9:58
  art of reading the room. Which is a phrase we
• 10:01
  hear all the time. Right, and I want to pause
• 10:03
  here because I think a lot of professionals hear
• 10:04
  the phrase read the room and roll their eyes.
• 10:07
  Sure, it's on every generic leadership blog on
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  the internet. Exactly. We all know what it means
• 10:12
  to generally gauge the mood of a meeting, but
• 10:15
  how do you actually execute that strategy when
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  the room is inherently biased against you? It
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  requires shifting from basic observation to strategic
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  empathy. Strategic empathy? Say more about that.
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  Well, Hindley realized that Having the mathematically
• 10:32
  correct answer in a Phinox meeting isn't enough
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  if the delivery mechanism triggers the audience's
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  biases. Right. Reading the room, in her context,
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  meant diagnosing the communication styles, the
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  risk tolerances, and the hidden agendas of the
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  people sitting across from her before she spoke.
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  Okay, here's where it gets really interesting
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  to me. I think some people hear... tailor your
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  message. And they interpret that as I have to
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  compromise my identity and water down my personality
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  to make other people comfortable. Which is a
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  huge fear for a lot of people. Right. But reading
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  the room isn't about compromising your identity.
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  It is the fundamental difference between just
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  broadcasting data and actually communicating
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  a message. Let's look at the mechanics of that
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  difference because it's key. Broadcasting is
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  standing in a room and shouting your truth or
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  your technical data regardless of whether anyone
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  is equipped to hear it. It's a one way street.
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  Yes. Communication, however, requires the sender
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  to encode the message in a way that the specific
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  receiver can accurately decode. OK, but how does
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  that actually play out in a highly technical
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  meeting? Like, how do you encode passion into
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  data? Let's use a hypothetical phenom scenario
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  based on her background. OK, let's hear it. Suppose
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  she spots a massive inefficiency in how an application
• 11:48
  is drawing cloud computing power. A broadcast
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  approach fueled by her natural passion. might
• 11:54
  be to go into a meeting and say, this architecture
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  is broken, it's incredibly inefficient, and we
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  need to overhaul the deployment pipeline right
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  now. Which, because of unconscious bias, just
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  gets coded as too loud or disruptive. Exactly.
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  The message is completely rejected. So strategic
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  empathy means reading that the room is full of,
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  say, risk averse financial managers. OK, so you
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  pivot. Right. You encode the exact same urgency
• 12:18
  into their language. You say, the current deployment
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  pipeline is generating a $10 ,000 a minute cost
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  overrun during peak hours. I have a risk mitigated
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  plan to optimize the architecture and recapture
• 12:28
  that capital. Oh, wow. That is brilliant. Notice
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  how you haven't lowered your ambition. You haven't
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  changed your ultimate goal at all. No, you've
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  just adjusted the frequency of the message so
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  the audience can actually hear the solution instead
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  of reacting to the volume. Exactly. That is just
• 12:43
  a master class in execution. But, I mean, reading
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  the room only works for the people actually sitting
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  in the meeting. How do you ensure your ideas
• 12:52
  have weight before you even open your mouth?
• 12:55
  Her second major strategy highlights the absolute
• 12:58
  necessity of developing strong alliances. Finding
• 13:01
  mentors and securing advocates within the organization.
• 13:04
  Yeah. But again, I'm trying to picture the real
• 13:07
  -world mechanics of this. How does a single software
• 13:10
  engineer who's already fighting self -doubt actually
• 13:13
  build a chorus of advocates. Well, you don't
• 13:16
  build advocates by pitching ideas cold in a high
• 13:18
  stakes environment. That's way too risky. Right.
• 13:20
  You build them through intentional localized
• 13:23
  socializing of your ideas beforehand. Meaning
• 13:25
  what exactly? If you know a major architectural
• 13:28
  decision is coming up, you seek out a senior
• 13:31
  engineer or a manager individually. Just a one
• 13:33
  on one. Yeah, you ask for their perspective on
• 13:35
  your data. You integrate their feedback. By the
• 13:38
  time the formal meeting actually occurs, you
• 13:41
  aren't a lone voice introducing a radical idea.
• 13:43
  You already have backup. Exactly. You have intentionally
• 13:47
  created a network of people who understand your
• 13:49
  value and your vision. When you speak, You have
• 13:53
  their implicit backing. You've pre -wired the
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  room. Perfect way to put it. And this combination
• 13:58
  of tailored empathetic communication and strategic
• 14:01
  alliances is what propelled her rapidly up the
• 14:04
  ranks to a VP position. It really works. It proves
• 14:08
  her core thesis, honestly. Inclusive leadership
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  isn't just a title. It's about someone who has
• 14:13
  fought through bias, ensuring that the voices
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  of their team members are heard too. It creates
• 14:17
  this amazing compounding effect. When a leader
• 14:20
  understands the mechanics of how ideas get silenced,
• 14:23
  they become hyper aware of making sure they don't
• 14:26
  replicate those same rigid structures for the
• 14:28
  engineers working under them. Totally. But to
• 14:31
  reach that VP level at companies with the scale
• 14:33
  of JPMorgan or Deloitte, I mean, you have to
• 14:35
  scale your influence. One -on -one socializing
• 14:38
  only gets you so far in an enterprise that big.
• 14:41
  Exactly. Hindley outlines two specific ways she
• 14:43
  scaled her visibility and they sit on totally
• 14:46
  opposite ends of the spectrum. One is very old
• 14:48
  school and one is very new school. Right. One
• 14:51
  is a deeply traditional human approach and the
• 14:53
  other relies on cutting edge technology. Let's
• 14:56
  start with the human element. She advocates for
• 14:59
  the immense power of a simple coffee chat with
• 15:03
  senior leadership. It sounds deceptively basic.
• 15:05
  but it is an incredibly potent mechanism for
• 15:08
  disarming the corporate hierarchy. I'm struggling
• 15:11
  to see how a simple coffee chat breaks through
• 15:13
  deeply ingrained unconscious bias, though. Like,
• 15:16
  if an executive has a preconceived notion of
• 15:18
  who you are, does a 15 -minute coffee really
• 15:21
  change that? It actually can. Can we dig into
• 15:23
  the psychology of why this works? It works because
• 15:26
  it fundamentally changes the environmental context.
• 15:29
  A formal presentation has stakes, It has an agenda,
• 15:33
  a strict timeline, and it triggers a professional
• 15:35
  threat response. Like executives are listening
• 15:37
  just to find the flaws in your pitch. Yes. A
• 15:41
  coffee chat strips away that corporate armor.
• 15:43
  When you invite a senior leader for a brief informal
• 15:46
  conversation, you are removing the immediate
• 15:49
  business stakes. You're just two people talking.
• 15:52
  Exactly. You are no longer just an engineer demanding
• 15:54
  resources. You are a human being asking about
• 15:58
  their career trajectory or seeking perspective
• 16:01
  on an industry trend. It humanizes you. It makes
• 16:04
  it much harder for their unconscious bias to
• 16:07
  rely on a two -dimensional stereotype because
• 16:09
  you are forcing them to interact with your multi
• 16:12
  - dimensional reality. It is a really targeted
• 16:15
  way to build alliances right at the very top.
• 16:17
  But I mean, taking that step requires significant
• 16:19
  courage. You are actively choosing to take up
• 16:22
  space on an executive's calendar. That can be
• 16:24
  very intimidating. Yeah. And while she is navigating
• 16:27
  these highly nuanced interpersonal human connections,
• 16:30
  she is simultaneously employing the exact opposite
• 16:33
  strategy to scale her influence. The tech side.
• 16:36
  Right. She advocates using AI as a strategic
• 16:38
  ally. This is where her engineering background
• 16:41
  truly shines. She doesn't just view AI as a utility
• 16:44
  for writing code or automating DevOps tasks.
• 16:46
  No, she takes it much further. She advises using
• 16:49
  artificial intelligence to actively refine human
• 16:52
  communication and navigate those biased environments.
• 16:56
  It is exactly like having a secret digital career
• 16:59
  coach sitting right on your desktop. That's a
• 17:01
  great analogy. Think about the mental friction
• 17:04
  of the two -loud paradox. If you have been penalized
• 17:07
  for your tone in the past, drafting an email
• 17:10
  to a difficult stakeholder becomes an exhausting
• 17:13
  exercise in second -guessing yourself. Oh, absolutely.
• 17:16
  You sit there rewriting the same paragraph for
• 17:18
  an hour. Yes. Hindoli realized you can literally
• 17:21
  feed that communication into an AI model to remove
• 17:24
  the emotional guesswork. The prompting process
• 17:27
  is what really makes this effective. You don't
• 17:29
  just ask the AI to write the email for you. Right.
• 17:32
  It's not a ghostwriter. You write your natural
• 17:33
  passionate thoughts, and then you prompt the
• 17:36
  AI with specific parameters. You ask, review
• 17:38
  this project proposal. Is the tone assertive
• 17:41
  but collaborative? Does the language convey urgency
• 17:44
  without sounding accusatory? How might a risk
• 17:47
  averse financial executive interpret this opening
• 17:49
  paragraph? Wow, so you are using the machine
• 17:52
  to test the encoding of your message before you
• 17:55
  broadcast it to a human. Exactly. It is absolutely
• 17:58
  fascinating that, in a deeply vulnerable struggle
• 18:01
  to be heard by other people, she recruited an
• 18:04
  artificial intelligence as a shield against their
• 18:07
  biases. If we connect this to the bigger picture,
• 18:10
  it perfectly encapsulates what modern leadership
• 18:13
  in the tech industry requires today. You need
• 18:15
  both sides. You cannot rely solely on raw technical
• 18:19
  output, and you cannot rely solely on networking
• 18:22
  charm. You have to master both ends of the spectrum.
• 18:25
  The human and the machine. You have to be brave
• 18:28
  enough to sit down for a highly personal informal
• 18:31
  coffee chat to disarm a senior executive. And
• 18:33
  you have to be savvy enough to use the latest
• 18:35
  AI models to meticulously refine your strategic
• 18:38
  messaging. It is that synthesis of the highly
• 18:40
  personal and the highly technical that creates
• 18:43
  true influence. Absolutely. So from the rigid
• 18:46
  precision of her software engineering roots to
• 18:48
  the creative expression of her dance performances,
• 18:51
  she took all those raw materials and built a
• 18:53
  leadership style that just refused to be silenced.
• 18:56
  She really did. She didn't lower her volume.
• 18:59
  She just figured out how to make her frequency
• 19:01
  undeniable. Undeniable is the perfect word for
• 19:03
  it. So what does this all mean for you? As you're
• 19:07
  listening to this deep dive, I want you to think
• 19:09
  back to that corporate meeting we visualized
• 19:11
  at the very beginning. The freezing cold room.
• 19:14
  Yeah. When you have the solution, are you just
• 19:17
  broadcasting your data and hoping the room is
• 19:19
  equipped to hear it? Or are you diagnosing the
• 19:23
  room, encoding your passion into their language,
• 19:26
  and relying on the advocates you've already built?
• 19:28
  It's a huge difference. Take a page out of Hindoli's
• 19:31
  playbook and audit your own mechanisms. And more
• 19:34
  importantly, challenge yourself this week. Find
• 19:37
  a senior leader in your organization whose trajectory
• 19:39
  you admire, push past the self -doubt, and send
• 19:42
  that daunting calendar invite for a coffee check.
• 19:45
  Just do it. You might be surprised at how much
• 19:47
  the room changes when you simply choose to take
• 19:50
  up space. And as you think about how to elevate
• 19:52
  your own voice, I want to leave you with a final
• 19:55
  thought to mull over regarding her use of technology.
• 19:58
  Okay, let's hear it. There is a pervasive anxiety
• 20:00
  right now. that artificial intelligence is going
• 20:03
  to make the workplace colder, more robotic, and
• 20:06
  fundamentally less human. Oh, you hear that everywhere.
• 20:09
  Right. But Hindley successfully used AI to navigate
• 20:14
  unconscious bias, to refine her emotional intelligence,
• 20:17
  and to ensure her empathy landed perfectly with
• 20:20
  her audience. That's a wild flip. It presents
• 20:22
  a fascinating paradox. Will the integration of
• 20:25
  AI in the workplace actually force us to become
• 20:28
  more empathetic, thoughtful, and inclusive communicators,
• 20:31
  rather than making us more robotic? That is a
• 20:33
  brilliant question to end on, because if a machine
• 20:35
  can help anyone perfectly encode their data,
• 20:38
  then the only very that truly differentiates
• 20:41
  your idea is the humanity and the conviction
• 20:43
  driving it. Never be afraid to let them hear
• 20:45
  it.