Amazon Behavioral Interview [Part 22] - Disagreeing With Your Manager (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about navigating a disagreement with a manager.

- **Identified Potential Concerns:** The feedback highlights potential concerns about the interviewee's response, specifically regarding its potential impact on how an Amazon interviewer might perceive the candidate's "bias for action."

  - The initial disagreement with the manager's unrealistic 15-minute response time could be perceived negatively, as Amazon highly values individuals who demonstrate strong initiative and a bias for action.
  - The interviewee's acknowledgment of the initial failure to meet the 15-minute target could be interpreted as a lack of commitment or a reluctance to fully embrace the manager's directive.

- **Suggested Improvements:** To address these concerns, the feedback suggests that the interviewee could have:

  - **Emphasized proactive problem-solving:** Instead of simply acknowledging the initial failure, the interviewee could have highlighted their proactive efforts to gather data, analyze the situation, and propose an alternative solution that was more realistic and achievable.
  - **Demonstrated commitment to the team's success:** The interviewee could have further emphasized their commitment to the team's success by describing how they actively engaged with stakeholders, addressed their concerns, and ultimately achieved the revised goal of a 90th percentile response time within one hour.
  - **Framed the disagreement constructively:** Instead of focusing on the initial disagreement and the perceived failure to meet the 15-minute target, the interviewee could have framed the situation as a learning opportunity and emphasized their ability to adapt and find a successful solution that aligned with the team's capabilities and customer needs.

- **Overall:** The feedback emphasizes the importance of demonstrating a strong bias for action, a commitment to achieving goals, and the ability to navigate disagreements constructively in an Amazon interview setting. By incorporating these suggestions, the interviewee can enhance their ability to effectively communicate their accomplishments and demonstrate their suitability for a role at Amazon.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Amazon Behavioral Interview [Part 15] - Going Above and Beyond For A Customer (Answer)

This video segment describes a situation where the interviewee went above and beyond to help a dissatisfied customer.

- **Identified a Customer Issue:** The interviewee encountered a platinum card customer who was extremely upset because their car, which was damaged while skiing, was not covered by the card's rental insurance due to a specific exclusion in the terms and conditions.
- **Went Above and Beyond:** Despite the company policy, the interviewee recognized the customer's frustration and proactively sought a resolution. They:
  - Investigated the issue and identified the relevant policy.
  - Engaged with internal stakeholders, including the product team and a senior executive, to advocate for the customer.
  - Successfully secured approval for a $4,000 statement credit to cover the repair costs.
  - Further impressed the customer by arranging for a check to be mailed directly, providing a more tangible and personalized gesture of goodwill.
- **Achieved Positive Outcomes:**
  - The customer was highly satisfied with the resolution and even became a testimonial for the company.
  - The company retained a valuable customer who was initially considering canceling their card.
  - The incident led to a review of the policy and improved communication regarding the scope of rental car insurance coverage.

This example demonstrates the interviewee's commitment to customer satisfaction, their ability to navigate internal processes, and their proactive approach to resolving customer issues.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Become An Engineer Influencer [Part 21] - Networking

Become An Engineer Influencer [Part 19] - What If Nothing Works?

Build Up Relationships

We highlight why building strong relationships and social capital is essential for career resilience, especially during layoffs. Here are the core points from the lesson:

- Relationships are a form of social capital, and having people who know you, like you, and will advocate for you can directly influence whether you survive layoffs or get new opportunities.
- Engineers often underinvest in relationship-building by staying heads-down, but proactively connecting with managers, skips, and peers across teams is increasingly important in today’s economy.
- Building relationships doesn’t require forced networking—it can happen naturally through code reviews, coffee chats, offsites, and one-on-ones if you are intentional about seeking opportunities.
- No matter how senior or skilled you are, everyone is expendable, so you should never rely on company loyalty alone and should continuously invest in relationships that outlast any single job.

Become An Engineer Influencer [Part 24] - Consulting

Notebook Interview Example: Ad Click Prediction

In this final part of the section, we bring all the core concepts together through a realistic, end-to-end ML interview example. The walkthrough demonstrates how to apply best practices across the entire notebook round—from EDA to model evaluation—with clarity, intention, and real-world relevance.

- In **EDA**, we go beyond surface checks by exploring unique value counts, visualizing target distribution, spotting class imbalance, and identifying trends like CTR by device type—ensuring every insight informs downstream modeling.
- In **feature engineering**, we thoughtfully create new features (e.g., age buckets, time-of-day flags), handle multicollinearity, and tailor transformations based on domain intuition, not just standard recipes.
- During **model training**, we justify our choice of random forest for its robustness and interpretability, apply cross-validation and stratified splits, and run grid search for tuning—demonstrating intention behind every step.
- In **evaluation**, we use metrics that align with the problem (precision, recall, F1, AUC-ROC, confusion matrix) and interpret results in context, showing we understand the real-world stakes of false positives and negatives.
- For **follow-up questions**, we proactively discuss improvements (e.g., deeper models, richer features), production monitoring (CTR by segment, drift detection), and handling class imbalance—proving we think like end-to-end ML practitioners, not just coders.

This example illustrates how to move from “checking boxes” to demonstrating thoughtful, production-aware problem-solving under real interview conditions.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Amazon Behavioral Interview [Part 29] - Building Trust With Stakeholders (Answer)

This video segment focuses on how to build trust relationships with colleagues and stakeholders.

- **Key Strategies:**

  - **Proactive Relationship Building:**
    - The interviewee emphasizes the importance of proactive relationship building by reaching out to stakeholders early on, even before needing to rely on them. This includes conducting preliminary research, scheduling introductory meetings, and taking the time to understand their individual goals and priorities.
  - **Demonstrating Integrity and Consistency:**
    - The interviewee highlights the importance of acting with integrity, following through on commitments, and being transparent about their goals and priorities. This builds trust and credibility with colleagues.
  - **Assuming Trust:**
    - While acknowledging the importance of earning trust, the interviewee believes in starting with a foundation of trust and assuming positive intent from others.
  - **Open and Honest Communication:**
    - The interviewee emphasizes the importance of open and honest communication, including being transparent about priorities and setting clear expectations.

- **Practical Tips:**

  - **Leverage tools like "The First 90 Days" to effectively identify and map stakeholders.**
  - **Conduct introductory meetings with key stakeholders to build rapport and understand their perspectives.**
  - **Focus on building relationships based on mutual respect and trust.**
  - **Prioritize consistency and follow-through on commitments.**
  - **Communicate openly and honestly about goals and priorities.**

This approach to building trust relationships emphasizes proactiveness, integrity, open communication, and a focus on understanding and meeting the needs of stakeholders.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Mock Interviews Are Non-Negotiable

To wrap up our preparation strategy, we dive into how to *structure* our prep effectively—because randomly studying “a bit of everything” doesn’t work.

- **Mock interviews are non-negotiable**: They’re the fastest way to sharpen your skills, build confidence, and improve how you communicate under pressure.
- **Avoid practicing in real interviews**: We should use mocks to make mistakes and learn, not high-stakes opportunities.
- **Find partners in your network or online**: Leverage communities or platforms like ADP List to schedule mock sessions with others.
- **Record your mocks**: Watching ourselves afterward reveals blind spots we may not notice in the moment—this step alone can lead to major improvements.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Amazon Behavioral Interview [Part 28] - Recovering From A Bad Decision (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they made a bad decision.

- **Strengths:**

  - **Owned the Mistake:** The interviewee openly acknowledged the mistake and took responsibility for the negative consequences.
  - **Demonstrated Self-Awareness:** They identified the root cause of the mistake – focusing solely on short-term revenue gains while neglecting long-term customer value and potential.
  - **Highlighted Corrective Actions:** The interviewee described the steps taken to rectify the situation, such as revising the incentive structure, improving call center response times, and implementing measures to address long wait times.
  - **Learned from the Experience:** The interviewee outlined the key learnings from this experience, including the importance of considering long-term customer value and gathering input from stakeholders before implementing significant changes.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have further quantified the impact of the mistake and the subsequent corrective actions. For example, they could have provided specific data on the improvement in customer satisfaction scores or the increase in customer retention rates after implementing the changes.
  - **Emphasize Positive Outcomes:** The interviewee could have further emphasized the positive outcomes of the project, such as the improved focus on customer lifetime value and the development of a more robust customer retention strategy.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions and learnings to relevant Amazon Leadership Principles, such as "Customer Obsession," "Learn and Be Curious," and "Ownership."

- **Overall:**

  - The interviewee effectively demonstrated their ability to acknowledge and learn from mistakes, take responsibility for their actions, and implement corrective measures.
  - By incorporating the suggested improvements, the interviewee can further enhance their response and increase their chances of success in the interview.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Framing The Dialog

We cover how promotions actually work, why they’re not guaranteed even if we’re performing well, and how to talk about them with our EM in a way that aligns incentives and builds trust.

- We explain that promotions require **both** performing at the next level **and** an organizational need for someone at that level—otherwise even great performers can’t move up.
- We note that promotion discussions require trust; bringing up promotions too early (e.g., in week one) backfires because the manager doesn’t yet know our work or commitment to the team.
- We warn that reorgs can derail promotion momentum when a new manager must advocate for us without shared history; coordinating between old and new managers helps mitigate this.
- We highlight that promotions are meant to signal that we already operate with higher scope, independence, and cross-org trust—not something to wish into existence.
- We emphasize framing promotion goals in terms of **increasing team impact** rather than title or compensation, since managers (especially in tight-budget environments) prioritize engineers who help them deliver more impact, not those who simply want advancement.

Amazon Behavioral Interview [Part 26] - Working Outside Of Your Domain (Evaluation)

This video segment analyzes an individual's response to a behavioral interview question about taking on a project outside of their core responsibilities.

- **Strengths:**

  - **Identified a Problem:** The interviewee effectively identified a critical issue: low employee engagement scores regarding promotion fairness.
  - **Took Initiative:** They proactively sought to address this issue by developing and implementing a new feedback system ("Perfolution").
  - **Demonstrated Collaboration:** The interviewee successfully collaborated with HR to implement the system and gather data.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have more clearly quantified the impact of the "Perfolution" system. Instead of simply stating that scores "went up," they could have provided specific data points, such as the percentage increase in employee satisfaction scores related to promotion fairness.
  - **Focus on Leadership:** The interviewee could have further emphasized their leadership role in driving the project, such as:
    - Motivating their team to participate in the feedback process.
    - Overcoming resistance or challenges encountered during implementation.
    - Communicating the results of the project to key stakeholders and securing buy-in for necessary changes.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions to relevant Amazon Leadership Principles, such as "Ownership," "Customer Obsession," and "Bias for Action."

- **Overall:**

  - While the interviewee demonstrated initiative and problem-solving skills, the response could be strengthened by:
    - Providing more specific and quantifiable data on the impact of the project.
    - Further emphasizing their leadership role and the challenges they overcame.
    - Explicitly connecting their actions to relevant Amazon Leadership Principles.

This analysis provides valuable insights into how to effectively communicate accomplishments and demonstrate leadership qualities in a job interview context.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

How Should I Practice For These Interviews?

In this lesson we talk about how AI is making previously time-prohibitive advanced data structures and algorithms newly relevant in coding interviews, and what that means for how you should prepare.

- **AI unlocks harder concepts by handling the boilerplate** — complex structures like tries, heaps, and LRU caches were rarely testable in a 45–60 minute interview because implementation alone could eat up 15–20 minutes; with AI generating that scaffolding in seconds, interviewers can now reasonably include them, making them worth studying once you've solidified the fundamentals.
- **What you actually need to demonstrate has shifted** — since AI handles much of the implementation, your value lies in recognizing when a given data structure is the right tool, clearly articulating *why* it helps (e.g. "a trie eliminates the factor of M by checking all patterns simultaneously through shared prefix traversal"), and writing precise prompts that reflect that understanding.
- **Master the basics first, and don't over-rotate on this** — the vast majority of interviews (roughly 95%) will not require these advanced concepts, so treat this as the final 5–10% polish layer, not the foundation; verification of AI output remains just as critical here as everywhere else in the interview.

ML Algorithm Example: K-means

In this segment, we walk through a high-level structure for implementing **k-means clustering from scratch**, focusing on clarity, modularity, and real-time reasoning. The emphasis is not on memorizing code, but on understanding the logic and being able to build it confidently.

- We start by defining an `__init__` method to store **core parameters** like the number of clusters (k) and max iterations.
- We **initialize centroids** by randomly selecting k data points; interviewers may also ask about smarter strategies like **k-means++**.
- In the **assign_clusters** step, we calculate distances from each point to every centroid and assign points to the nearest one.
- The **update_centroids** method recomputes each centroid as the mean of its assigned points; optionally, we may include a **loss function** to track clustering quality.
- The **fit** method loops through initialization, assignment, and updates until convergence, while **predict** assigns clusters to new data after training.
- Most importantly, we aim for **clean, modular, and well-structured code**, and narrate our logic during the interview to demonstrate deep understanding—not just syntax.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Round Structure ML Algorithm

In this section, we dive into what makes the **ML algorithm coding interview** distinct from traditional coding rounds. It’s a focused, high-pressure test of both our theoretical depth and practical coding ability.

- We’ll be asked to **implement ML algorithms from scratch**—no scikit-learn, no TensorFlow—just raw code.
- This round tests both our **understanding of the algorithm’s inner workings** and our ability to **code it cleanly and efficiently**.
- With only **45–60 minutes**, we’re expected to focus on **core logic and code structure**, not perfection or full library-level implementations.
- The scope is typically **limited to common algorithms** like linear regression, logistic regression, or k-means—so it's highly trainable.
- With preparation, we can excel by knowing the **key steps, math, and edge cases** behind each algorithm and practicing how to express them in code.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Why Coding Matters In ML

In this new section on **coding fundamentals**, we explore why strong programming skills are essential for machine learning engineers. While ML involves data and models, it’s also deeply rooted in real-world software engineering.

- We clarify a common myth: **ML engineers are still engineers**, and are expected to meet the same coding standards as traditional software engineers.
- Our **software engineering background is an asset**—if applied correctly, it can set us apart from more research-focused ML candidates.
- **Coding matters in ML** because models must be integrated into production systems, maintained, and optimized for performance and scalability.
- We’re reminded to consider **algorithmic complexity**, especially when building pipelines or serving models at scale.
- The key takeaway: **don’t underestimate LeetCode-style coding prep**. Many ML interviews, especially at top tech companies, still include traditional algorithm and data structure questions. Strong coding skills are non-negotiable.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Maximize Your Equity And Benefits In Tech [Part 21] - Bottom Line

There are a few essential things to keep in mind when managing retirement accounts. Here are the core points from the lesson:

- 401(k)s are effective for automating savings and deferring taxes, especially in high tax brackets, and it's important to capture the employer match and invest contributions
- Pre-tax vs. Roth decisions aren’t one-size-fits-all, but choosing wisely can reduce your lifetime tax bill with the help of smart withdrawal strategies
- Mega backdoor Roths allow large tax-free contributions if your plan supports them, while backdoor Roths offer smaller contributions with more complexity and caution required

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Your Rating Should Never Be A Surprise

In this lesson, we explain why your performance rating should *never* be a negative surprise and how maintaining transparent, ongoing conversations with your EM prevents career-derailing outcomes.

- We emphasize that if you expect “meets” and end up on a PIP, something has gone catastrophically wrong—and it’s almost always preventable through proactive communication.
- We recommend regularly asking for a *hypothetical rating* (“If you had to rate me today, what would it be?”) while explicitly acknowledging it’s not a guarantee—this makes managers comfortable giving candid signals.
- We highlight that good managers are usually 80–90% accurate with these hypothetical ratings, giving you strong insight into where you truly stand.
- We note that engineers who avoid this conversation out of discomfort effectively “walk themselves off a cliff” by choosing to stay in the dark.
- We reinforce that building trust through vulnerability, consistency, and strong 1:1 habits unlocks your manager’s willingness to give honest performance signals—ensuring you’re never blindsided.

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.