Yash Parikh-Applied Science Manager @ Amazon

Yash Parikh, Applied Science Manager at Amazon, joins the Apex Perspective podcast to break down why successful machine learning and AI initiatives require more than sophisticated algorithms. They require a deep understanding of customer behavior, measurable business outcomes, and the ability to communicate technical value in ways that customers and business leaders can understand. Drawing from experience across Deutsche Bank, SAP Concur, and Amazon Payments, this conversation explores how technical leaders identify meaningful customer problems, anticipate needs customers have not yet articulated, adapt solutions across different markets, and turn promising technical ideas into initiatives that earn organizational support. *In this episode, we cover:* 1. Why customer experience should guide technical development, and how even the most sophisticated engineering solution can fail to create value if customers find it difficult to understand, adopt, or use in their everyday lives. 2. Why understanding customer needs involves more than simply listening to feature requests, and how anticipating needs customers have not yet expressed can create opportunities for meaningful product innovation. 3. How customer behavior, payment preferences, and regulations vary across countries and customer segments, and why a solution that succeeds in one market may not translate directly to another without understanding its unique constraints. 4. Why identifying the right problem requires a measurable business outcome, and how vague objectives such as improving customer experience become actionable only when tied to specific metrics that demonstrate whether a solution is actually moving the needle. 5. How communicating complex technical ideas in simple business language can determine whether an initiative receives leadership support, including why engineering leaders need to explain the customer problem, expected value, and potential return in a way that decision makers can understand. 6. Why the transition from individual contributor to engineering manager requires rethinking where time creates the most value, shifting from direct technical execution toward securing stakeholder alignment, developing talent, mentoring engineers, and helping promising initiatives gain organizational support. 7. How psychological safety and open communication encourage innovation, and why engineering teams need the freedom to challenge existing approaches, propose early-stage ideas, and question the status quo while remaining accountable to evidence and measurable results. 8. Why strong technical leadership requires balancing conviction with adaptability, encouraging experimentation and new ideas while being willing to change direction when customer feedback, data, or results challenge the original hypothesis. #Amazon #AppliedScience #MachineLearning #ArtificialIntelligence #AI #EngineeringLeadership #DataScience #CustomerCentricity #ProductInnovation #BusinessStrategy #TechnicalLeadership #Leadership #Podcast