Meet Natalie, Talent Acquisition Leader for Science at Amazon Ads

Meet Natalie Matushevsky, a Boston-based Talent Acquisition Leader specializing in technical and executive recruiting. She currently serves as the head of science recruiting for Amazon, focusing on experienced talent for Amazon Ads, IMDb, and Devices and Services. During her time at Amazon, Natalie has helped multiple teams hire talent to support the development of new products from initial concept to full-scale deployment.

Natalie offers unique insights into what makes Amazon Ads an exciting place for scientists to innovate and grow. In this interview, she shares her perspective on the work scientists do, how candidates can prepare for interviews, and the culture that defines scientific roles at Amazon Ads.

I think what sets Amazon Ads apart is the close interface our scientists have with advertising customers and consumers. Unlike at other large tech companies where scientists may work on abstract problems, our scientists’ work directly influences how brands connect with customers. They can tackle challenges at scale, from helping self-published authors find their audiences to optimizing campaigns for major corporations like Hyundai or Martha Stewart's brand.

The challenges that our scientists are looking to solve are always customer-centric and align with new or enhanced products to delight our customers. I don’t think this is unique to science per se. Customer Obsession is a consistent mission for everyone at Amazon; it’s in our DNA.

This year, the team is leaning into generative AI to optimize the holistic customer cycle for both large and small brands. They’re thinking about questions like these: “How can we make a unique, customized campaign relevant to a brand that’s less known and has a limited budget? How do we enhance a deeper understanding of funnel optimization for existing customers? Which tools can we launch and pilot that are easily accessible for self-service? Where can we leverage new technologies to enhance creative vision?” The topics are long-standing questions in the advertising industry, but the “how” is rapidly changing. We're creating "key turn" approaches to products for customers as small as self-published authors and as large as household name brands. AI innovation is fostering a unique and curated experience for both types of customers.

The biggest thing our scientists appreciate is being able to see if their techniques or applications work in real time. At Ads specifically, we value applied versus theoretical science, so our scientists can test techniques and ideas and take them to market to test, learn, and refine them to improve the overall customer experience. Seeing theoretical concepts come to life while working closely with engineering, design, product, and distribution leaders empowers scientists to develop a well-rounded career in bringing new concepts to life. And of course, as part of that, there’s a deeper understanding of “impact at scale” and what it actually takes to launch a new product when working through real-world concerns and trade-offs along the way.

Amazon has a high degree of ownership, visibility, and accountability. The ability to understand trade-offs, given real-world concerns, while not an official Amazon Leadership Principle (LP), is something that we look for in individuals. When we’re recruiting scientists, my team members are thinking, “How creative can you be? Can you communicate your vision and mobilize others around you to understand what you’re trying to achieve? Can you learn from your mistakes and move forward?”

Working at an organization like Amazon can be challenging at times for scientists who come directly from academia. Suddenly the deadlines and additional communication along the way are compressed into a shorter time frame. It’s an adjustment for some. At the same time, it’s a big opportunity for people to hone new skills. There’s value derived in rigor, application of fundamentals, and knowing when to raise your hand to collaborate or seek sage advice across teams. We emphasize the "think big, start small" mindset, which is a fun problem to solve when considering how many customers our products reach. Amazon is challenging, but it’s also exciting!

We look for different skill ranges depending on level, product type, and team. At the experienced level, we’re definitely looking for individuals who can draw on prior experience and tie it back to our LPs. For example, having a high degree of “Learn and Be Curious” and “Ownership” will help scientists succeed at Amazon. But in short, we look for smart, hard-working, curious people who are motivated to solve complex problems.

We also look for people who can understand the scale of Amazon and have proven impact at scale at the more experienced level. Explaining the scale at which we work, when compared to other companies, is probably the most challenging concept to explain to potential candidates. I didn’t truly grasp it until I joined.

The biggest challenges we have when looking for top talent tend to be domain-specific—like finding the right expertise in Computer Vision and Optical Engineering. In the landscape of generative AI, depending on what the business is trying to solve for, we might look at scientists who have worked on a specific type of large language models. However, we’re open to bringing scientists on board from a variety of different backgrounds.

Not necessarily. It depends on the specific role and where the team is in terms of product development. Understanding basic frameworks is important, but we hire scientists from diverse backgrounds, including financial services, healthcare, academia, and large and small tech companies. Of course, if you’re going to be leading an established team of scientists in ad tech, prior experience is nice to have for a faster ramp time, but generally our scientists come here from different walks of life and go on to have diverse careers here.

Take the preparation seriously. Depending on level and experience, our interviews span five to six hours and focus on both LPs and technical capabilities. When preparing candidates, I encourage them to review the domains they’ve outlined in their resumés, brush up on baseline coding, and review the publications they’ve written or contributed to, as our interviewers might want to gain a deeper understanding of prior research experience. I also encourage candidates to think outside of the box. If your prior experience doesn’t support a question being asked by an interviewer, it’s okay to say that it’s not a problem you’ve encountered before, but you should still have a go at solving it theoretically. Interviewers are interested in how you think and what retrospectives you can offer based on your vision, experience, and thought process. Focus on what you've delivered, where you've made an impact, and how you've handled failures or challenges.

Our science community at Amazon spans far wider than just Ads. Science as a job family is very much established at Amazon, and there’s a large network of colleagues who collaborate through events, both internally and externally. Internally, we have a yearly machine learning conference for our scientific talent that has been running for several years. It’s a fantastic platform that shares what each team is doing across the industry. Additionally, there are regular panel conversations and tech talks for scientists to upskill and grow their networks. We’ve had some fantastic innovators speak to our teams on a wide array of topics; some continue to collaborate with our teams today.

What I love about science at Amazon is that we think about hiring beyond the standard roles. I joined Amazon to help scale the Amazon Scholars program, and my former team has done a fantastic job of expanding Amazon Scholars to associate professors and post-docs. This means that innovators who are committed to academia can spend their time during the summer, on sabbatical, or part-time during the year and work alongside our scientists to test and try theory to application.

Having supported several teams that have successfully launched amazing new products, it’s a great feeling to be part of work that positively impacts our customers while also stacking the building blocks for successful careers in science.