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2020-02-07

Why a Data Science Engineer Becomes a Product Manager


As an engineer, I've been working on machine learning (ML) and data science at Arm Treasure Data over the past three years. You can find more about me in the portfolio page. Basically, my job responsibility, which overlaps with my personal interests a lot, has been somewhere between engineering and customer-facing work for bringing advanced technologies such as ML and IoT to an enterprise-grade big data analytics platform.

Based on the experiences that I had during the three years, I recently decided to changed my role from Engineer to Product Manager (PM) within the same organization. This post explains the reason why I made the decision. In my opinion, the PM is a unique option for those who are looking for an opportunity of delivering real-world ML solutions.

TL;DR

  • I've been continuously looking for a way to bridge a gap between scientific theory and real-world practice (e.g., ML algorithm vs. its application).
  • My experience in the industry told me that considering user experience is crucial to successfully productize complex technologies.
  • To formally practice user experience-focused product development in the ML and data science domain, I have decided to switch my role from Data Science Engineer to PM.
  • Product management is one possible, lesser-known way to achieve meaningful outcomes by fully leveraging your ML and data science expertise.

My backbone: Bridging a gap between theory and practice

Most importantly, I have had a strong motivation on filling a gap between scientific theory and real-world practice for a long time, and I've always chosen a position where I can work in the middle of scientifically and technologically stimulating techniques and advanced, innovative applications.

When I first studied ML, I really enjoyed learning materials and creating some "toy" applications. Likewise, people are initially so excited about emerging technologies such as Big Data, AI, and Blockchain. But, since there is a huge difference between theory and practice, in reality, it is not always trivial for practitioners to translate technological innovations into real-world applications. At university, I was frustrated with myself as I had no clear ideas of how to overcome the challenge, and that's why I became a data science engineer to explorer the field of ML more in the industry.

The motivation doesn’t change until today, even when I put my foot into the field of IoT last year. As an engineer, IoT is so exciting, and I feel the area has a huge potential to dramatically change our life. However, at the same time, I first had zero ideas of how to build valuable, profitable applications on top of the technology. Thus, what I have experienced after the company was acquired by Arm is highly challenging but enjoyable, insightful activities; working in the emerging area with many experts I've never collaborated with is exactly the process of bridging theory and practice.

Key finding at the industry: User experience is the matter

Thanks to the great opportunities and colleagues, I have experienced a lot at an intersection of ML/IoT and real-world applications. For example, the experience of creating an ML-based out-of-the-box application told me that optimizing a whole experience for a certain persona is highly important to deliver the right product to the right people. Eventually, I realized how designing a better user experience is important to make complex technologies useful and meaningful.

The same principle can also be applied to IoT. As I reported in "What I've Seen at IoT Solutions World Congress 2019", a gap between PoC (theory) and production (practice) is the biggest, widely recognized issue in this field. For me, one of the biggest reasons is clearly in the process of user interface and experience design. Everyone creating IoT products is so excited about their underlying technology, but investing in the hardware products is not that easy for consumers as they usually do for software. Thus, the early majority would become particularly careful about IoT products, and the developers are required to provide a sophisticated, seamless experience through well-developed products.

Therefore, I can confidently say that being user-driven is a key step to connect engineering and product so that we can deliver a valuable, meaningful application by ensuring a great user experience on top of the complex technologies.

Current goal: Be a product developer in a formal sense

For the reasons that I mentioned above, my current goal is to familiarize myself with UX-focused product development and deliver a useful application to the end-users. To be more precise, I'm passionate more about being in charge of every single step of product development life-cycle, from understanding user needs and prototyping to clarifying product requirements and making it real along with a validation process.

Industrial trend shows that the role of PM, UX designer, and engineer is more overlapped in these days. On that point, I looked for a new position that enables me to work at an intersection of these multiple responsibilities. I strongly believe such hybridized work eventually leads a big jump from PoC-grade application to a real product.

As a consequence of much consideration and conversation with several people I previously worked together, I started thinking PM would be the best position for me to formally work somewhere between product, design, and engineering, and have the most meaningful experience for my future career.

So what?

To acquire the ability to effectively productize complex technologies, I decided to become a PM. In fact, the scientific and technological aspect (e.g., accuracy, scalability) is not everything about ML and data science. It should be noted that, though, I cannot deepen my thoughts in this way and make such a strategic decision without three-year experience in the industry.

We ML & data science people can deliver the value in many different ways. The major activities we can easily imagine include: analyzing complex real data and gaining insights, implementing a scalable ML system, and designing algorithms. Meanwhile, since good product management and user experience design largely expand the possibility of these outcomes, it'd be great if somebody in the team could have an understanding of the underlying technologies. In reality, however, a product person having an ML background is still not common as far as I can observe.

Hence, PM can be a unique, exciting area as an ML & data science career. Interestingly, even for the first one week or so, there were many new findings that I've never noticed before in the company. So, I'm really excited to explore and deep dive into the world of product management from now on.

  Author: Takuya Kitazawa

Takuya Kitazawa (a.k.a. takuti) is working on machine learning, data science, and product development at Arm Treasure Data.

Opinions are my own.

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