How to Break Into Data Science (Interview Prep Masterclass from ex-Amazon and Walmart Data Scientist) - w/ Karun

Karun Thankachan begins with the scale of his mentoring work: thousands of bookings and several thousand one-to-one conversations with aspiring data professionals. That exposure shaped his view that most candidates do not lack another certificate; they lack a coherent project, a credible explanation of their decisions, and a job-search system they can sustain.

The most common portfolio mistake is using a toy dataset exactly as a tutorial used it. Karun wants a project tied to a recognizable business problem, with messy data, constraints, and an explanation of why the result matters. A hiring manager should be able to see judgment, not merely that the candidate executed someone else's notebook.

He separates model metrics from business metrics. Accuracy, precision, recall, or error can describe a model, but a company still needs to know whether the work changes revenue, cost, risk, retention, or another decision. Before selecting an algorithm, he recommends a literature review and a reasoned argument for why that method fits the data and objective.

Karun ranks strong projects first and networking immediately after. In a market flooded with applications and AI-tailored resumes, a referral or informed introduction can get a real person to inspect the evidence. Networking cannot rescue weak work, but good work is easier to miss when nobody inside the company knows why it is relevant.

A polished portfolio site is the final layer, not the foundation. Karun would first build and document projects, then make the resume and outreach clear, and only then invest in presentation. Candidates often spend weeks styling a homepage because it feels productive while avoiding the harder work of producing a defensible analysis.