From Amazon to Intuit: Keerthan Reddy’s Journey to Becoming a Senior Data Scientist

Securing a senior data scientist role at a multi-billion dollar company is no small feat, and for Keerthan Reddy, the journey was both challenging and rewarding. While the strong data science foundation he built during his tenure at Amazon was helpful, Keerthan sought this new opportunity and overcame the challenges to fulfill his professional ambitions and personal goals.

Despite the challenges and what we believe was a “pretty intensive interview,” Keerthan’s strategic preparation paid off, earning him the desired position at Intuit and a 20% salary increase.

His story highlights the importance of structured learning, leveraging resources effectively, and staying resilient through even the most demanding interview processes. And, of course, how he utilized us to prepare for the journey:

“If I have [an] interview tomorrow, I’ll immediately go to that resource, and I will look all the questions on Interview Query because that’s like a one-stop-shop kind of thing. Where you [can find] different types of questions [on a single page].”

Keerthan Reddy’s journey in data science began with his role as a data scientist at Amazon, where he spent over four years honing his skills. He credits his time at Amazon for building a strong foundation in data science and preparing him for the challenges of a senior role at Intuit.

He recalled Amazon’s rigorous focus on its 14 (now 16) leadership principles that shaped his approach to problem-solving and team collaboration. These principles, such as “Deep Dive,” “Bias for Action,” and “Ownership,” were pivotal in defining his current (soon-to-be-ex) role at Amazon.

At Amazon, Keerthan excelled at analyzing complex data and delivering results quickly, often balancing trade-offs to meet tight deadlines. He also stated that the interview process to join Amazon was a test of his resilience and expertise, consisting of five one-hour rounds. Each round combined technical case studies with assessments of his alignment with Amazon’s leadership principles, emphasizing his ability to think critically and have a bias for “action, ownership, and delivery results.”

Although highly skilled, Keerthan encountered several challenges during Intuit’s rigorous senior data scientist interview. The multi-round process, including technical and behavioral evaluations, tested him on various fronts. According to Keerthan, the most demanding part was the “craft exercise,” where he had to build and analyze a model under a strict time constraint of 90 minutes. This required rapid analysis, coding, and model development under pressure. As he said, “If someone [is] asking me to code and present something [on the spot], it [is] always challenging.”

He also had to present the solution for the next 60 minutes to a panel of four, which understandably added another layer of challenge, requiring clear communication and confidence. The other interview rounds, while challenging with their focus on machine learning algorithms, practical knowledge, product sense problems, and stakeholder communication, were not as demanding as the craft exercise, according to Keerthan.

He, however, emphasized how his interview concluded on a positive note, with the hiring manager round being “very casual” and “more like a conversation talking about specific customer problems.”

Keerthan’s preparation for the senior data scientist role at Intuit was thorough and strategic. With his skills and access to Interview Query’s rich resources, he overcame every technical and behavioral challenge presented to him at the interview.

Using Interview Query for Technical Practice

Keerthan extensively used our platform to sharpen his technical skills. He focused on coding problems, particularly SQL and machine learning (ML) questions, which were integral to the role. IQ’s well-organized sections of easy, medium, and hard SQL queries helped him build a solid foundation. He also highlighted how deep-diving into ML Algorithm Learning Paths and reviewing the mathematical computations behind popular algorithms like logistic and linear regression were helpful to his journey.

In his words, “Interview Query has a lot of stats and ML questions. I’ve been using those questions for my preparation too, especially [for] the technical aspects [of the interview].”

Reviewing Past Projects

Understanding that his previous experience was a key part of the interview, Keerthan revisited his past data science projects to be able to discuss them in-depth. He expressed, “The first half an hour [of the phone screen round] was based on my previous project, digging deep [into them].”

This included analyzing how he had applied data science techniques to solve real-world problems, which proved useful during the behavioral rounds.

Focusing on Product and Behavioral Scenarios

Keerthan prepared for the product sense and data science behavioral questions by reflecting on past challenges in his work, especially around product development and stakeholder communication. He practiced articulating how he navigated complex situations, worked with cross-functional teams, and made decisions that impacted the product and business.

While talking about the interview rounds, Kerthan stated, “The next round was a product sense [round]. It was [mostly about] understanding the challenges. It was more like a behavioral understanding [of] the challenges that I went through, how I dealt with [them], what product challenges I had while working on the team, and how [I] work with the stakeholders.”

Time Management and Presentation Skills

Given the time constraints of the craft exercise, Keerthan practiced managing his time efficiently while building models. He also worked on presenting his solutions clearly and confidently, anticipating potential questions from the panel.

Broad Research Across Platforms

In addition to our platform, Keerthan utilized other platforms like Data Interview for structured courses on machine learning and statistics. This broad approach gave him a well-rounded preparation, ensuring he was not only ready for specific company-based questions but also equipped with deep domain knowledge.

Keerthan provided us with a detailed overview of his senior data scientist interview at Intuit, and we’ve already touched on several aspects throughout the article. Now, let’s take a deeper dive to give you a clearer and more comprehensive picture of his experience.

Keerthan’s interview process at Intuit was a comprehensive, multi-stage evaluation that tested both technical and behavioral skills. It began with a phone screen, where the first half focused on his past projects, and the second half involved coding, particularly in Python.

The on-site interview started with a challenging craft exercise, where Keerthan had to build a churn prediction model from scratch in 90 minutes. He then presented the model and discussed a previous research project for the next 60 minutes.

Following a lunch break, the interview continued with three additional rounds:

• A deep dive into his earlier presentation and technical aspects, including machine learning algorithms like logistic regression and linear regression.
• A product sense round, which focused on his approach to product challenges, teamwork, and stakeholder communication.
• A casual, insightful discussion with the hiring manager, centered on customer problem-solving.

The entire process tested Keerthan’s technical expertise, problem-solving abilities, and communication skills, with each round presenting a unique challenge.

Keerthan Reddy’s journey from Amazon to securing a senior data scientist role at Intuit highlights the power of thorough preparation, resilience, and adaptability. Through a rigorous interview process and strategic use of our resources, Keerthan successfully navigated technical and behavioral challenges, demonstrating both his expertise and ability to communicate complex solutions. His story serves as an inspiring example for anyone looking to advance in the field of data science.