Finding Your Optimal Career Using Bayesian Optimization: The Exploration-Exploitation Principle

Why Choices Are Difficult

"I can't find what I want to do." "Is this really the right choice for me?" "What career would leverage my strengths?"

These questions represent universal struggles that many face at life's crossroads. Particularly in today's world where options seem infinite, finding the path that suits you has become increasingly difficult.

I studied machine learning at Kyoto University and later became an entrepreneur. Through this journey, I realized that the "Bayesian optimization" algorithm I was researching could be applied to life choices. This concept, which could be called the "science of choice," has significantly helped my own career decisions.

In this article, I will introduce a practical approach to efficiently finding your optimal path by applying the machine learning concepts of "exploration and exploitation" to career design. I'll focus on accessible thinking strategies rather than mathematical complexity.

There are three main reasons why life and career choices have become particularly challenging in modern society.

First, the diversification of options. In the past, career paths were often straightforward—"join a local major company and work until retirement." Now, we have freelancing, entrepreneurship, multiple income streams, remote work, international employment, and more. Industries have become specialized, with many professions emerging that didn't exist a decade ago. While increased choices expand freedom, they also make decisions more difficult.

Second, the challenge of accelerating change. The skills and market value needed ten years ago when I was researching machine learning are vastly different from those required today. The evolution of AI technology, particularly with the emergence of generative AI like ChatGPT, is fundamentally transforming many professions. Making "future-proof" choices feels like aiming at a moving target.

Third, and perhaps most fundamental, is the difficulty of self-understanding. "I don't know what I like" or "I don't know what I'm good at" are universal challenges everyone faces. This is especially serious for younger generations who must make important choices with limited life experience.

Self-understanding requires feedback from others, I believe. When young people tell me they don't know their strengths, I always respond: "Looking inward alone won't reveal your talents. It's through various experiences and external feedback that your strengths become apparent."

For these reasons, many people fall into three common traps when making career choices:

1. Action paralysis: Unable to choose anything in pursuit of the perfect option
2. Reactive choices: Making decisions based solely on trends or peer influence
3. Dependency on others' opinions: Letting parents, supervisors, or mentors dictate your path

In this context, how can we efficiently find our optimal path? Machine learning optimization theory offers illuminating insights.

The Science of Choice: Bayesian Optimization Thinking

What is Bayesian optimization? While the technical definition is complex, it essentially represents a method for finding optimal solutions with minimal trials. At Kyoto University, Bayesian optimization was one of my research topics, often used for automatically tuning hyperparameters (configuration values) in machine learning models. As a side note, I was applying Bayesian optimization to optimize experimental conditions in manufacturing.

Why is this concept relevant to life choices? Because career selection and machine learning optimization problems share surprisingly many commonalities:

1. Trials come with costs: Machine learning is constrained by computational resources, while careers face time and opportunity costs
2. Unknown function shape: In machine learning, we don't know the shape of the function we want to optimize; in life, we cannot accurately predict outcomes of choices in advance
3. Presence of noise: Machine learning measurements contain noise, while life outcomes are influenced by luck and external environment

At the core of Bayesian optimization is the concept of balancing "exploration" and "exploitation". In machine learning, these are called "Exploration" and "Exploitation." Let me explain this with an everyday example.

Exploration and Exploitation: The Restaurant Example

Imagine you've moved to a new city. You want to find delicious restaurants, but your time and money are limited. How can you efficiently discover "the best restaurant for you"?

Exploration strategy: Try a different restaurant each time. This allows you to experience diverse cuisines and price ranges, but risks encountering disappointing options.

Exploitation strategy: Repeatedly visit restaurants you've already enjoyed. This ensures satisfaction but may cause you to miss even better places.

The wise approach is to balance both. Initially, try various restaurants (exploration), then once you find some good options, primarily visit those (exploitation) while occasionally trying new places (continued exploration).

Bayesian Optimization in Career Choice

Career selection can be approached with exactly the same principle:

• Exploration: Try various jobs, projects, and work styles
• Exploitation: Further develop promising paths

The beauty of Bayesian optimization lies in learning from past trials to determine the next step. In restaurant-hunting terms, if you discover "Italian cuisine suits me," you might try different Italian restaurants next.

Similarly in careers, if you find that "jobs involving direct customer interaction suit me," you might explore other positions with this element, gradually improving the precision of your choices.

Finding life's optimal solution is precisely the kind of optimization problem that Bayesian optimization excels at—"high trial cost with a function shape that isn't explicitly known." Career choices involve significant costs, and we cannot accurately predict which choice will lead to what outcome in advance.

Applying this principle, the career design process can be restructured as follows:

1. Emphasize exploration in early stages, trying various possibilities
2. Engage in exploitation and deepen expertise when you find a promising area
3. Adjust the balance between exploration and exploitation as you approach the optimal solution

When people ask me, "I want to choose a career path, but I haven't determined what I want to do," I suggest "increasing the number of exploration samples" and "marking what doesn't suit you with × rather than trying to find what you like." This is a practical application of Bayesian optimization thinking.

So how can we practically implement "exploration" and "exploitation" in careers?

Career "Exploration" Approaches

In the career exploration phase, gaining as diverse experiences as possible is crucial. This is similar to increasing the number of samples in machine learning—more data leads to more accurate judgments.

However, it's not just about quantity. Improving the quality of exploration is equally important. Here are specific approaches for effective exploration.

Methods for Efficiently Gaining Diverse Experiences

1. Accumulate small trials

Time and resources are finite. Therefore, gaining new experiences in small ways before making major career changes is effective:

• Side jobs or project-based work: Experience new fields while maintaining your main job
• Hackathons or competitions: Experience new skills and collaboration styles in short, intensive periods
• Volunteer activities: Opportunities to encounter different values and environments
• Short-term internships: Learn about industries and organizational cultures from the inside

My own experience participating in hackathons and internships before starting a company greatly helped me understand my aptitudes. I was uncertain whether to pursue a path as an engineer or on the business side. However, at hackathons, I often won with ideas despite not having the strongest technical skills. I was valued for my ability to formulate business strategies from an engineering perspective.

2. Deliberately place yourself in different environments

To increase exploration efficiency, it's important to consciously choose diverse experiences:

• Experience organizations of different sizes: Large corporations and startups require vastly different abilities and values
• Try different roles: Not just specialized positions, but management or project leadership roles
• Touch different industries or fields: Apply your expertise in different contexts
• International experience: Working in different cultures broadens your values and thinking

When I joined SHIFT, a Japanese IT venture company, as a new graduate in 2016, I was assigned to the president's office after just two months. This was a valuable experience as I could leverage my engineering knowledge while learning about management perspectives and leadership, expanding my range of possibilities.

3. Utilize simulated experiences

Methods to "try before you commit" can be effective:

• Books and online courses: Gain basic knowledge
• Simulations and role-playing: Experience in safe environments
• Interviews with mentors and practitioners: Learn from others' experiences
• Shadowing: Observe practitioners up close for a day

For example, before actually starting my company, I deepened my understanding of entrepreneurship's meaning and necessary skills by attending events for entrepreneurs and listening to successful business owners.

The "Marking with ×" Method to Improve Exploration Quality

Particularly effective in the exploration phase is the approach of "identifying what doesn't suit you." While it's difficult to directly find "what you want to do," it's relatively easy to determine "what you don't want to do" or "what doesn't suit you."

This "marking with ×" elimination method is something I always emphasize. "It's easier to mark what you don't like or aren't suited for with × than to find what you like."

For example, make judgments like these:

• Marketing: "I enjoy data analysis, but I'm not good at creative aspects" → △
• Programming: "I'm good at logical thinking, but long hours of concentrated work tire me out" → △
• Sales: "I like talking with people, but I'm weak under pressure" → ×
• Education: "Supporting others' growth is what I enjoy most" → ○

By marking areas that "don't suit you" with ×, you'll begin to see suitable paths among the remaining options. I myself considered becoming a researcher at one point, but realized "I'm better suited to tackling various challenges than concentrating on a single theme long-term," which led me to entrepreneurship.

Collecting Feedback During the Exploration Stage

Another important element in improving exploration quality is external feedback. Self-perception has limitations, and objective evaluations from others can provide clues to discovering your strengths.

Specific methods for collecting feedback include:

• Formal evaluations from supervisors and colleagues
• Advice from mentors and seniors
• Post-project reflections
• Reactions from social media and professional communities

From my own experience of being selected for the president's office at SHIFT as a new graduate, I learned that I was valued for my business and communication skills, which started me thinking about entrepreneurship. Thus, talents and aptitudes you're unaware of are often discovered through external feedback.

Timing and Methods for Career "Exploitation"

After sufficient exploration comes the "exploitation" phase—the stage for maximizing the potential you've discovered.

Criteria for Deciding "This Direction Seems Right"

Determining when to transition to the exploitation phase is challenging. However, if you notice the following signs, it may be worth deepening that direction:

1. Sustained interest: Your interest doesn't fade over time
2. Immersion: You experience "flow state," forgetting time while engaged
3. Sense of growth: You feel steady improvement with effort
4. External validation: You receive positive feedback and recognition from others
5. Future prospects: You have confidence in the market value and future of the field

If multiple of these signs apply, it might be a good time to move into the exploitation phase in that direction.

In my case, when I discovered my interest in AI and education, I found that "I could work on it forgetting time," "I felt fulfilled witnessing people's growth," and "I could leverage knowledge of both technology and education." These signs led me to enter the "exploitation" phase through entrepreneurship in the AI education field.

Methods for Deepening in the Exploitation Phase

In the exploitation phase, it's important to enhance specialization and uniqueness in your chosen direction. Specific methods include:

1. Systematic learning

   • Learn systematically from fundamentals to applications
   • Solidify knowledge through authoritative books and courses
   • Stay updated with the latest research and trends

2. Accumulation of practical experience

   • Apply knowledge in actual projects
   • Learn from both failures and successes
   • Gradually take on more challenging tasks

3. Participation and contribution to communities

   • Build networks with specialists in the same field
   • Present at study groups and conferences
   • Share insights (blogs, books, etc.)

4. Building unique perspectives

   • Critically examine existing common knowledge and methodologies
   • Establish your own approaches and theories
   • Develop unique viewpoints by incorporating knowledge from other domains

In my case, after discovering my interest in AI and education, I chose "entrepreneurship" as my form of "exploitation." While systematically learning about AI, I practiced its application in educational contexts. Through this process, I was able to establish a unique position not just as an AI technician but as an "AI education specialist."

Balancing Exploration and Exploitation

It's important to continue moderate exploration even after entering the exploitation phase. Completely stopping exploration risks narrowing your vision and losing adaptability to changing environments.

Especially in our rapidly changing modern society, the following balance can be effective:

• Main specialization (exploitation): 70-80% of time and resources
• Exploration of related new areas: 20-30% of time and resources

For example, while working as a marketing specialist, you might allocate time to explore the latest AI tools and data analysis methods.

Maintaining this balance allows you to deepen your expertise while preserving adaptability to environmental changes. Even while managing my AI education company, I consciously reserve time for "exploration" of new technology trends and educational methods. This has ultimately led to business innovations.

The Equation of Liking and Being Good At Something

An unavoidable question in career selection is the debate: "Should I pursue what I like, or what I'm good at?" This is one of the questions I'm frequently asked.

The Pros and Cons of Making "What You Like" Your Career

The advice "do what you love" sounds ideal at first glance. However, reality isn't so simple.

Challenges of making "what you like" your career include:

1. What you like doesn't necessarily have market value

   • Even if you love music or travel, few people can earn sufficient income from these interests

2. What you like may become less enjoyable when it becomes work

   • The approach and pressure differ between hobbies and work

3. The feeling of "liking" something can change

   • What you like in your youth may not continue throughout your life

From my experience, "you can continue to enjoy what you like by using money earned from what you're good at to pursue it."

I myself loved music (band activities), but rather than making it my profession, I chose to continue my musical activities with income earned from my education business. Thanks to this choice, my passion for music has remained pure.

The Mechanism of "What You're Good At" Becoming "What You Like"

What I want to emphasize is the mechanism whereby if you make what you're good at your job, you'll come to like it. This has psychological backing.

1. Evaluation cycle: What you're good at tends to be valued by others. Being valued feels good, leading you to engage with it more.

2. Joy of mastery: You improve faster at what you're good at, experiencing a sense of mastery. This feeling of growth enhances intrinsic motivation.

3. Formation of self-image: When the self-image "I'm good at this" is established, it becomes part of your identity, naturally connecting to the feeling of "liking."

What I always convey is that "if you do what you're good at, you'll generally be valued. Being valued makes you happy. Happiness leads to liking it, creating a positive cycle."

The Importance of Being Valued and Its Psychological Impact

Humans are social beings, and recognition from others is a powerful motivational factor. Especially in career choices, this external validation is a significant element.

The psychological effects of validation include:

1. Increased self-efficacy: The conviction that "I can do this"
2. Sense of belonging: Recognition as a member of a professional community
3. Growth mindset: Motivation for further improvement

From my experience at SHIFT, being valued for my communication abilities and leadership skills made me interested in "management," an area I hadn't previously considered. This experience eventually led to my choice to become an entrepreneur.

Therefore, in career selection, considering not just "whether you like it" but "whether you're likely to be valued" is important. If you can create a cycle of being valued for what you're good at, it has a high probability of transforming into something you "like."

Practical Optimization Techniques for Decision Making

Based on the theories discussed so far, I'd like to propose practical methods for decision optimization. I'll focus particularly on new strengths in modern society and methods to practice exploration and exploitation in daily life.

New Strengths in an Era of Change

With the emergence of generative AI like ChatGPT, many jobs are changing. However, this doesn't mean human value is diminishing. Rather, it signifies a shift in the layer of abilities required.

Abilities that gain value in times of change include:

1. Problem definition ability: The power to define what problems should be solved
2. Systems thinking: The ability to see the big picture and understand relationships between complex elements
3. Domain knowledge integration: The ability to combine technology with specific domain knowledge
4. Ethical judgment: The ability to consider social impact
5. Creativity: The ability to think beyond existing frameworks

These abilities are higher-order capabilities compared to mere repetitive tasks and are difficult for AI to replace. Consciously developing such abilities becomes important for future careers.

Methods to Practice Exploration and Exploitation Daily

Here are concrete approaches to implementing the principles of exploration and exploitation in daily life.

1. Practicing the 20% Rule

Try time allocation inspired by Google's "20% rule."

• 80% of work time on main projects (exploitation)
• Remaining 20% on exploring new technologies or domains

During this 20%, try touching on new areas related to your main domain. For example, while working in marketing, spend one day a week learning about data science.

2. Building T-shaped Skill Sets

A T-shaped skill set refers to having deep expertise in one area (vertical bar) while maintaining broad knowledge across multiple related fields (horizontal bar). This embodies the balance between exploration and exploitation.

For example:

• Deep expertise (exploitation): Sales
• Broad knowledge (exploration): Marketing, product knowledge, psychology, data analysis

3. Regular Self-assessment and Course Correction

To maintain optimal balance between exploration and exploitation, regular self-assessment is essential.

• Quarterly skill inventory
• Annual major career reflection
• Checking alignment with industry trends

If these reflections indicate "more exploration needed," increase exploration; if "deepen specialization," shift toward exploitation.

4. Habitualizing Small Experiments

Develop a habit of accumulating small experiments before big decisions.

• Weekend projects
• Volunteer activities
• Online course participation
• Side jobs or freelance projects

Through these small experiments, you can verify your aptitude for areas of interest at low risk.

Toward Long-term Career Building

Finally, let's consider career building from a long-term perspective. A common challenge many face mid-career is the choice between "deepening specialization or moving into management."

The principles of exploration and exploitation are effective for this choice as well.

1. Explore both

   • Try leadership roles
   • Experience managing small teams
   • Engage in mentoring or educational activities

2. Exploit based on aptitude

   • If you find joy in being valued for specialized skills, pursue the path of specialization
   • If you find joy in supporting others' growth, pursue the management path

3. Consider hybrid careers

   • Positions involving both technology and management
   • Managers or consultants in specialized fields
   • Entrepreneurs leveraging specialized knowledge

I myself chose a hybrid career, maintaining a technical background while incorporating both elements through "teaching AI to people." This choice was possible because I realized during the exploration stage that "I find joy in supporting others' growth."

Conclusion

Optimizing decision-making is not a one-time choice but a continuous process of repeating exploration and exploitation. Just as machine learning optimization algorithms approach optimal solutions through trial and error, we discover our optimal paths through various experiences and feedback.

What's important is not seeking the "perfect choice," but making decisions after sufficient exploration and maximizing exploitation within those decisions. And maintaining the flexibility to readjust the balance between exploration and exploitation as the environment changes.

For us living in rapidly changing modern society, this "science of exploration and exploitation" becomes a survival strategy beyond mere theory. Why not incorporate thinking methods like Bayesian optimization to find the optimal solution for the unknown function that is your own life?

And finally, to summarize my thoughts: "It's natural to be unable to answer when asked what you like. Instead, explore widely and mark what doesn't suit you with ×. Then, deepen what you're valued for being good at, and enjoy the process of growing to like it." This may be the core of a sustainable career.