On this interview, we communicate with Preetham Reddy Kaukuntla, Employees Knowledge Scientist at Glassdoor, about navigating the evolving calls for of AI-driven decision-making. Preetham shares how statistical evaluation, experimentation, and machine studying converge to ship measurable impression, and gives insights into mentoring knowledge scientists towards business-oriented considering. From balancing short-term outcomes with long-term scalability to shaping the long run position of AI management, his views make clear each the strategic and sensible sides of knowledge science.
Discover extra interviews right here: Jay Dawani, Co-Founder & CEO at Lemurian Labs: Pioneering Accessible and Sustainable AI Growth
Your journey displays a robust mix of statistics, experimentation, and machine studying. Are you able to stroll us via a defining second the place these pillars converged to ship a crucial enterprise impression?
One defining second got here through the overhaul of our notification platform at Glassdoor, the place the problem was to enhance engagement with out growing message fatigue. We started with statistical evaluation of historic engagement knowledge, which revealed key behavioral segments, for instance, high-value job seekers who responded to sure job sorts at particular instances of day. This step recognized not solely the “what” but additionally believable “why” patterns behind dips in engagement.
From there, we designed managed experiments to check completely different suppression guidelines, timing changes, and content material variations. One take a look at, for instance, in contrast each day versus adaptive ship schedules for prime segments, measuring click-through, apply begins, and churn over a number of weeks.
The profitable methods have been then operationalized into an ML-driven concentrating on pipeline that dynamically adjusted ship frequency and rating based mostly on real-time engagement scores. Inside three months, the system diminished redundant sends by 30%, saved $150K yearly in electronic mail prices, and elevated utility begins from notifications by 18%, a transparent instance of how statistics, experimentation, and machine studying can construct on one another to ship measurable enterprise worth.
As a Employees Knowledge Scientist, management goes past technical expertise. How do you mentor junior knowledge scientists to develop a business-oriented mindset?
I encourage junior knowledge scientists to consider themselves as companions in decision-making, not simply technical executors. We begin by clearly defining the enterprise context, what choice is at stake, who’s making it, and the way success can be measured. This framing helps shift the mindset from “I’m building a model” to “I’m influencing an outcome.”
We additionally focus closely on trade-offs. For instance, a marginal accuracy acquire might not be well worth the added complexity if it delays deployment or erodes interpretability. I ask them to at all times take into account the “last mile,” how their work can be consumed, by whom, and below what constraints.
One sensible train I exploit is having them current their findings twice, as soon as to a technical viewers and as soon as to a enterprise viewers. The flexibility to adapt the identical perception for 2 very completely different teams is a talent that multiplies their impression. Over time, they study that affect and belief typically matter greater than technical sophistication alone.
Constructing end-to-end AI options typically requires balancing short-term deliverables with long-term scalability. How do you handle this pressure?
The stress between velocity and sustainability is a continuing in AI initiatives. My method is to run two parallel tracks, one centered on delivering one thing tangible shortly, and one other on constructing the infrastructure and processes that may enable the answer to evolve with out breaking later.
Within the short-term observe, we goal for purposeful prototypes, minimal however helpful options that show worth early. Within the long-term observe, we spend money on knowledge high quality, structure design, and automation, figuring out that these investments forestall future bottlenecks.
What makes this work is transparency. I commonly share with stakeholders the dangers of neglecting scalability and the advantages of doing foundational work early. After they see that this method reduces rework and accelerates future launches, it turns into a lot simpler to safe buy-in. In the long run, the quickest option to ship long-term impression is to plan for it from day one.
Are you able to share an instance of a challenge the place the impression wasn’t instantly seen however proved transformative over time?
A great instance is the event of ML-driven rating fashions for Glassdoor’s group content material. Initially, the challenge’s metrics seemed flat as a result of the algorithm prioritized relevance and high quality over quantity, that means fewer however extra focused posts have been proven.
Within the first month, engagement per session didn’t spike, and a few stakeholders questioned the shift. Nevertheless, over the subsequent six months, we noticed a 25% enhance in significant participation (multi-comment threads with job-related dialogue), 15% progress in repeat group visits, and a notable carry in sentiment scores from person surveys.
This slow-burn success got here from specializing in long-term person worth slightly than speedy clicks. It additionally diminished moderation overhead by 20% as a result of surfacing higher-quality posts led to fewer experiences and disputes. At this time, the ML-ranking framework is a cornerstone of our group technique, influencing not solely which posts are proven but additionally how we advocate discussions in electronic mail and push channels.
How do you determine between mannequin complexity and interpretability in high-stakes situations?
I view complexity as a software, not a default. The place to begin is at all times the only method that may credibly meet the target. Less complicated fashions have benefits, they’re simpler to elucidate, preserve, debug, and audit.
In high-stakes environments, whether or not the chance is monetary, reputational, or regulatory, interpretability typically takes precedence over a small enhance in predictive accuracy. That’s as a result of the price of a unsuitable choice isn’t simply an error fee; it’s the belief of the individuals counting on the output.
That mentioned, complexity is just not off the desk. If it delivers a considerable and justifiable enchancment, we’ll use it, but it surely should include mechanisms for rationalization and oversight. In different phrases, complexity has to earn its place.
What excites you most concerning the evolving intersection of AI and enterprise decision-making?
We’re coming into an period the place AI methods can transfer from being passive observers to energetic individuals in decision-making. As an alternative of simply offering evaluation, they’ll simulate situations, advocate actions, and predict downstream impacts in actual time. This creates alternatives for extra adaptive, forward-looking methods.
What excites me most is the potential for collaborative intelligence, the place AI handles scale and sample recognition, and people convey context, ethics, and judgment. The actual transformation will occur when these methods are designed not just for accuracy but additionally for readability and alignment with organizational values. That’s the place AI stops being only a software and turns into a trusted associate in shaping path.
With AI instruments democratizing knowledge entry, how do you see the Employees Knowledge Scientist position evolving within the subsequent 5 years?
The position will shift from “builder” to “architect.” As automation, pre-trained fashions, and no-code instruments turn out to be extra succesful, the differentiator for senior knowledge scientists can be downside choice, resolution design, and governance.
I see Employees Knowledge Scientists spending extra time orchestrating multi-model ecosystems, making certain methods are honest and explainable, and guiding cross-functional groups in utilizing AI responsibly. We’ll even be those setting guardrails, defining what issues AI ought to remedy, the way it needs to be evaluated, and when human intervention is important.
In different phrases, the job can be much less about producing outputs and extra about making certain that the outputs produced are the suitable ones.
How do you foster a tradition of steady studying and experimentation inside knowledge science groups?
It begins with decreasing the obstacles to experimentation. Groups want entry to scrub knowledge, the suitable instruments, and frameworks that make testing concepts easy. However infrastructure alone isn’t sufficient; you additionally need to form the mindset.
I make it clear that failed checks should not failures in the event that they produce studying. We maintain common “learning showcases” the place individuals share experiments that didn’t work as anticipated, together with the insights gained. This normalizes the concept that progress is constructed on iteration.
Over time, this creates an surroundings the place curiosity is rewarded, risk-taking is supported, and innovation is fixed, not simply one thing we do when there’s further time.
When you have been to design a “Data Science Leadership Playbook,” what would the primary three chapters be?
- Outline the Downside With Precision – Imprecise questions result in imprecise solutions. Make investments time in sharpening the query earlier than beginning the evaluation.
- Earn Belief Relentlessly – Your affect comes from credibility. Be clear, ship constantly, and personal each successes and errors.
- Lead By means of Others – Multiply your impression by empowering your workforce to assume independently, make selections, and take possession.
Lastly, what’s a private mantra you depend on when navigating complicated, ambiguous challenges?
“Progress over perfection, clarity through iteration.” I’ve realized that ready for the proper resolution typically means lacking the window for impression. As an alternative, I give attention to taking the most effective subsequent step with the knowledge accessible, measuring the result, and refining from there. This method retains momentum alive and creates house to adapt with out dropping path.
In fast-moving environments, adaptability is as essential as accuracy, and iteration is the way you obtain each.
