During my job search as a new graduate, I aimed to become a versatile professional who could excel anywhere. I began my career in sales at a software consulting company, where I felt I could develop the skills necessary to become a professional in the workforce. While I found satisfaction in a job where I could leverage interpersonal skills and proposal abilities, as the years went by, I felt that I lacked a distinct specialization that resonated with me. As I pondered about potential areas of strength, I noticed that companies at our client sites were all focusing on data utilization, which led me to believe that there would be significant demand for it in the future. Drawing from my past studies in statistics during my university years, I enrolled in a business school to self-study data science, diving into the world of data science, which was still not widely recognized at the time.

Before joining Reazon, I worked at five different companies. I was involved in data analysis in various fields such as social games, call centers, and healthcare, all honing my practical skills along the way. Among these experiences, I found that social games, in particular, provided an environment for detailed analysis due to the comprehensive data available, including user registration, daily login status, and playtime, all within a virtual space. Since I personally enjoy gaming, I wanted to specialize in data science for games, which led me to join Reazon.

I got acquainted with Mr. Izumi, the representative of Rudel, and Mr. Shinohara, the hiring manager at Reazon, through a business-oriented social networking site, and we connected over a meal. This meeting happened during a period when significant game releases were impending, and they expressed their desire to elevate the level of data analysis. Intuitively, I felt that "interesting things could be done here." Data science is crucial for making a business impact, and I believe that in an environment where results are confronted directly, one can deliver even greater value. Recognizing the chance for such challenges, I concluded that Reazon provided the environment to do so and decided to join.

Reazon's gaming business has a culture of pursuing daily sales and achievements, with stakeholders earnestly contemplating how to achieve results every day. The company culture involves diligently using data analysis to iterate with high accuracy, aiming to improve user satisfaction and sales. Thus, it's an environment where data scientists can thrive.

As the head of the AI & Data Science Department, I have two main missions: data analysis and AI implementation. Regarding data analysis, I am building an analytical infrastructure for Reazon's businesses, enabling data utilization. Subsequently, I am creating a mechanism where everyone involved in the business can operate a data-driven business by visualizing data through BI dashboards. I analyze business situations from the perspective of users and data, identify issues, and collaborate with business stakeholders to propose ideas for strategies, while deeply committing to the business.

For example, in data analysis for social games, it's crucial to ensure that users can enjoy playing the games. We check on a daily basis what percentage of logged-in users are playing the main content. If this percentage decreases, it indicates that users may be losing interest, prompting us to delve deeper into the underlying reasons. Subsequently, we share the real-time analysis results with title owners, collaborate closely to propose hypotheses for issues and countermeasures, and then monitor the improvement in numbers after implementing these measures.

With advancements in technologies like large-scale language models and generative AI, we are at a turning point where the way people work is changing significantly. By researching the latest AI technologies and applying them to Reazon's operations, we aim to enhance the value of our services and improve operational efficiency. For instance, we are striving for operational efficiency through initiatives such as using generative AI for illustration creation, developing document AI, and utilizing AI for testing tasks in development work. Additionally, we are considering incorporating AI into our services. For example, integrating AI into games to automatically answer users' inquiries. The goal is for humans to focus on thinking for service improvement while AI handles all routine tasks.

The satisfaction lies in being able to work as a data science expert at the forefront of the business. Moreover, I find great fulfillment in seeing initiatives based on data science reflected in the business, manifesting visibly as business results. Previously, I mainly worked as a data scientist in outsourced companies, where the scope of duties was often limited by contractual obligations. As a result, it was challenging to delve deeply into the business, particularly in terms of proposing initiatives after analysis or utilizing predictive models. At Reazon, however, there is a focus on business commitment, allowing data scientists to be involved in creating business impact. Additionally, since Reazon operates a wide range of businesses in-house, there is a wealth of data available. The company has also established a robust environment and system for data management, ensuring high-quality data. This might seem unexpected, but it's not something to take for granted. High-quality and diverse data, coupled with a data-driven culture, are crucial elements for the growth of data scientists and represent unique strengths of Reazon.

One memorable moment was when I spoke at the business seminar of the "Tokyo Game Show 2023," the largest event in the gaming industry. I delivered a lecture on data-driven game operation using the hit title "Blue Rock Project: World Champion"

I believe that through data analysis, the services desired by users will become more clear, and the user experience will improve. When using a service, there might be moments when everyone thinks, "I wish it could be more like this". It's common to feel stress or inconvenience without realizing it, but data analysis can visualize these issues. By identifying and improving the points where users feel inconvenience, we can develop a service with high satisfaction.

The issue is a lack of colleagues to work with. The scale and number of our business at Reason are rapidly growing, and there are many tasks where we want to utilize data. However, there is a significant shortage of data scientists who can handle these tasks, and there is a shortage of talent in the market as a whole. Since there are still unexplored areas in the use of data science, I want to increase the number of colleagues who share the vision of Reazon and take on challenges.

For data analysis, we are looking for people who can be passionate about the business. Data scientists are often thought to need skills in statistics and programming above all else. Indeed, these skills are necessary, but they can be improved by anyone with training up to a certain level. At the same time, there's another important skill called domain knowledge, which refers to knowledge about the business or industry. No matter how much technical skill you have, without domain knowledge, you can't deeply commit to data analysis for business growth. So, I think those who have a strong interest in the business and can passionately pursue it are suitable. Whether it's a hobby or a job, if you have ever been passionate about something, you can become the strongest talent if you can learn data science techniques.

For AI engineers, I think those who have a strong interest in technology are suitable. They are always following new technologies and services in the world and constantly thinking about how to use them.

I think it's the fun of the job and the chance to get opportunities. It's not just about reporting the results of data analysis, but also being able to get involved in the measures beyond that, and the fulfilling environment where you can commit deeply to the business to the point where you can't produce value without getting involved.

Data science mainly plays a role in finding problems and improving them, and improving the accuracy of decision-making, but if you make a mistake, you can easily become a disliked entity that only points out problems. In order to work on an equal footing with everyone who is seriously engaged in the business, as a data scientist, I value understanding the business deeper than anyone else. To put it simply, I'm thoroughly trying to become the heaviest user of that business.

I believe that data-driven business operation can be realized for the first time by combining hypotheses born from an overwhelming user perspective and objective facts by data, and proposing measures.

By nature of my job, I have many opportunities to communicate with people from other departments, and I feel that there is a good inconsistency in the background and experience of the managers. I think it's a company that gives opportunities to people who have the passion to say, "I want to complete this job and grow," rather than because they have a high academic background or have certain experiences. Right after I joined the company, I was entrusted with all the data analysis tasks for the release of Blue Lock, and I was able to take on the challenge of building an analysis environment at my own discretion. As a manager, I try to give as many opportunities for challenges as possible while covering risks. The culture of Reazon is that more and more opportunities come to people with a challenging spirit, and those who have completed the tasks can further rise, which is the charm of Reazon.

Currently, I am involved in data analysis not only as a manager but also as a player, but there is a limit to the amount of work that one person can do. My goal is to increase the number of colleagues who can understand the business, analyze data, and propose measures, and to power up both the quality and quantity of work as an organization. In the field of AI implementation, I want to apply the technology that is advancing every day to our business ahead of the industry, and contribute to external branding that AI utilization is progressing. I want to continue challenging so that the world recognizes that both data analysis and AI are great at Reazon.