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Tell me about youself

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Why consulting?

What attracts me to consulting is the opportunity to combine analytical problem-solving with making a real impact on business decisions.

During my internship at Rasa, I analyzed over 30,000 transactions to identify inefficiencies in the company's fulfillment operations. I developed a cost model and presented a recommendation to management that projected an 11% reduction in costs per order.

What I enjoyed most about that experience wasn't just analyzing the data. It was taking those findings, understanding what they meant for the business, and developing a practical recommendation.

That's what draws me to consulting. I want to continue solving complex business problems, working with different teams, and using technology and data to help organizations make better decisions.

Why PwC?

What stands out to me about PwC is how the firm combines technology and business expertise to solve important problems for its clients.

I was particularly interested in PwC's recent work with Salesforce on AI-powered customer service. What caught my attention was that the focus wasn't simply on introducing AI, but on integrating it into existing business processes to improve efficiency and customer experiences.

That approach really resonates with me because, through my experience in data science, I've developed an appreciation for how important it is to make technology practical and useful for the people relying on it.

I also connect with PwC's emphasis on building trust and working collaboratively. I think the opportunity to learn from experienced professionals while contributing to technology-driven business solutions would be a great fit for my interests.

Tell me about a time you experienced conflict within a team. How did you resolve it?

As an equity research officer at Texas Marketrics, I was responsible for sorting analysts into sector teams based on their preference form. The form was first come, first served, and the AI sector was oversubscribed, so some people who chose it as their first choice couldn't be placed there. After the teams were announced, one analyst who had been placed in energy pushed back pretty hard and kept trying to get switched into AI. Instead of just telling him the decision was final, I tried to figure out what he actually wanted, and it wasn't the AI sector specifically, it was to work on AI. So I suggested he cover a vertical AI company within the energy industry. I was actually interning at one at the time, SotaOG, so I offered to help him find similar companies. He took it well and spent the semester researching an energy AI startup. What I got out of it is that when someone pushes back, the thing they're arguing for usually isn't the thing they actually want.

Tell me about a time you demonstrated leadership.

"As an equity research officer at Texas Marketrics, I was responsible for leading and supporting more than 20 analysts across five sectors.

One challenge was that I didn't come from a traditional finance background, but part of my role involved teaching concepts like valuation and discounted cash flow analysis. I knew that if I wanted to lead the analysts effectively, I needed to understand these topics well enough to explain not just how they worked, but why.

So, over winter break, I taught myself the material from scratch. I then developed a structured curriculum with presentations and study guides to help analysts build their skills. Throughout the semester, I also reviewed their research and provided feedback on their projects and final presentations.

By the end, the quality of research was much more consistent across the five sectors, rather than depending heavily on who was leading each individual team.

What I learned is that leadership isn't necessarily about being the most knowledgeable person in the room. It's about taking responsibility for your own gaps and putting in the work to help other people succeed."

Tell me about a time you faced an unfamiliar or ambiguous problem.

"At Convergent, UT's tech accelerator, I was the product manager for a team developing a sports partner-matching app.

About a month into development, we discovered that a very similar product already existed in the market. Since one of Convergent's requirements was that our product had to be original, we couldn't continue with our initial idea.

That put us in a difficult position because we had already invested a month of work, and we still needed to build an MVP and prepare our final pitch by the original deadline.

As product manager, I helped the team pivot toward a study buddy-matching app with AI-powered study tools. The biggest challenge was figuring out what we could realistically accomplish with the time remaining.

I went back to the user interviews I'd conducted and researched comparable products like Quizlet to identify which features mattered most. Originally, we'd planned several AI-powered study games, but I decided to prioritize one polished game rather than spread our resources across multiple unfinished features. I worked with our engineers and designers to adjust the scope and focus on what would best demonstrate the product's value.

Ultimately, we completed the MVP and delivered our pitch on time at demo day.

Tell me about a time you made a mistake or failed. What did you learn?

During my internship at Rasa, I worked on an inventory forecasting project to help the company better anticipate when it needed to reorder ingredients.

I analyzed historical usage and order data for over 20 products and calculated how quickly different ingredients were typically used up. The goal was to give the team a more proactive way to manage inventory rather than waiting until stock was running low.

However, after presenting my work, a stakeholder pointed out something I'd overlooked. My forecasts were based on historical averages, but I hadn't accounted for factors like promotions or seasonal spikes in demand. For example, a promotion could temporarily increase sales of certain products, which meant the historical average wouldn't necessarily predict how quickly their ingredients would run out.

I realized I'd focused too heavily on the data itself without fully understanding the business context behind it.

That feedback changed how I think about analytical problems. Now, I recognize the importance of speaking with stakeholders early, understanding what drives the numbers, and challenging my assumptions before building a solution.

Tell me about a time you received constructive feedback. How did you respond?

"During my internship at Rasa, I worked on an inventory forecasting project to help the company better anticipate when it needed to reorder ingredients.

I analyzed historical usage and order data across more than 20 products to estimate how quickly different ingredients would run out, with the goal of making inventory management more proactive.

After presenting my initial analysis, a stakeholder pointed out something I'd overlooked. My forecasts were based on historical averages, but I hadn't accounted for factors like promotions that could temporarily increase demand for certain products.

I realized that while my calculations were accurate based on the data I'd used, I hadn't fully considered the business context behind those numbers.

So I went back and revised the forecast. First, I excluded periods when promotions had temporarily inflated demand. Then, I separated the order data by quarter and developed individual inventory forecasts for each quarter to better account for seasonal differences.

The revised approach gave us forecasts that were more tailored to different times of the year rather than relying on one overall average.

What I learned is that good analysis isn't just about getting the calculations right. It's about understanding the factors driving the data and being willing to adjust your approach when someone brings a perspective you hadn't considered."

Tell me about a time you had to meet a tight deadline or manage competing priorities.

"At Convergent, UT's tech accelerator, I was the product manager for a team developing a sports partner-matching app.

About a month into development, we discovered that a similar product already existed. Since Convergent required our product to be original, we had to abandon our initial idea and start over.

The challenge was that our deadline hadn't changed. We still needed to build a minimum viable product and prepare a pitch for demo day, but we'd already lost a month.

As product manager, I was responsible for deciding what to prioritize. We pivoted to a study buddy-matching app with AI-powered study tools, and I used the user interviews I'd conducted and market research on similar products to identify which features would be most valuable.

Originally, we'd planned several AI-powered study games. But given the limited time, I decided we should focus on building one polished game rather than spreading our resources across multiple unfinished features.

I worked with our engineers and designers to narrow the scope and make sure we were aligned on what needed to be completed.

Ultimately, we finished the MVP and delivered our pitch on time at demo day.

What I learned is that managing a tight deadline isn't necessarily about doing everything faster. It's about identifying what's most important, making deliberate trade-offs, and keeping the team focused on those priorities."

Tell me about a time you used technology or data to solve a problem.

"During my internship at SotaOG, an energy technology startup, I worked on a project involving sensor data from oil wells and compressors.

One challenge was that the data wasn't always reliable. Some readings were missing or contained physically impossible values, which could cause problems if the data was used to build models or make operational decisions.

To address this, I helped develop a Python validation pipeline that automatically checked incoming sensor data for anomalies before it was used in further analysis.

When I tested the pipeline on one sensor dataset, it flagged 830 out of 862 readings—about 96.3%—as missing or invalid. That was a significant finding because it showed how easily unreliable data could make its way into an analysis.

I also built a Power BI dashboard that visualized normal and abnormal readings so the operations team could more easily identify where the problems were occurring.

The result was a more standardized and repeatable way to identify data-quality issues rather than relying on manual checks or someone noticing an unusual reading.

What I learned is that technology creates the most value when it solves a practical business problem. In this case, the goal wasn't just to build a Python pipeline—it was to help the team make more reliable decisions using their data."

Tell me about a time you had to learn something new quickly.

"Last semester, I applied for and was accepted into an equity research officer position at Texas Marketrics, where I'd be responsible for leading and supporting over 20 analysts across five sectors.

The challenge was that I come from a statistics and data science background rather than a traditional finance background. Part of my role involved teaching financial concepts like valuation and discounted cash flow analysis, which I wasn't yet comfortable explaining.

Since I had limited time before taking on the role, I spent my winter break teaching myself those concepts from scratch.

My goal wasn't just to memorize formulas. I wanted to understand why the methods worked, because I knew the analysts would have questions, and I needed to be able to explain the reasoning behind them.

Once I felt confident with the material, I developed a structured curriculum, including presentations and study guides. I also provided feedback on analysts' research projects and final presentations throughout the semester.

By the end, the quality of research was more consistent across the five sectors, rather than depending heavily on who was leading each team.

What I learned is that one of the most effective ways for me to learn something quickly is to approach it as though I'll eventually need to teach it to someone else. That forces me to understand the fundamentals rather than just memorize the process."

Do you have any question for us?

I was reading about PwC's recent work with Salesforce on Agentforce Contact Center, particularly how AI agents are being integrated into customer service operations. Has that shift toward AI-driven solutions changed the types of projects your team works on or the skills you look for in new consultants?

Looking back at when you started your career, what's one piece of advice you wish you'd received earlier?

What is it about PwC's culture that has kept you motivated to work here?