Why am I so happy to be jobless?

10 min readOct 31, 2022

Since all the big tech companies aren't doing so well, Meta didn't give a return offer after graduation. I'm jobless now again. Though going back to a job search black hole is daunting, I realize it offers a chance to rethink my life and career, and so does identifying the growth since I wrote My 10-year vision(1). If not, I'm pretty sure I would happily set my butt in Meta without giving other careers a second thought. Here is my learning I would love to share.

Deep reflection on my own career experience

Since Meta gives me the entire career package, I need to dissect it to understand what I like about Meta. I start with a basic question, "if I ought to sacrifice, what will I choose?"

I separate it into two categories, must-have and good-to-have.

# Must have
Learning: I would love to have ML or SWE learning branched out from my data science skills. I want to have learning opportunities (research, engineering)  that are outside of the main job functions.Nonhierarchical work environments: I like to challenge my boss.Have autonomy: I can deep dive into working (get into the workflow), usually technical position + a bottom-up cultureCurious people: People who love to learn and have a curious mindset. # Good to have
Strategy thinking: Product analytics + decision-making powerFunny colleaguesDrama free: No need to deal with too many people’s issues. It’s tiring.Good perks: food, housing, transportation.

Now I have a base to start. Next, I start thinking of where those priorities come from. Through comparing and contrasting different experiences, I realize that

Though I love to do practical things, I appreciate curiosity to know (scientist mindset) more than an intention to succeed (business mindset.) I want to have the environment to acknowledge not knowing, feel okay about it, and communicate it with people who appreciate it too.
Even working with the most amazing people, I feel dry when my learning curve decreases. Learning technical skills matters.
Even if my work is impactful, I'm not motivated if I do a repetitive and boring job. What to do with data doesn't matter that much. On the other hand, I can tolerate smaller impacts as long as I learn a ton.

Hence, I conclude that my priority in jobs will be scientific & technical learning > relationship learning (communication, management) > impact > salary. What I see from this process is, there are so many things about myself that I wish I can be (be an impactful person), but not who I really am (care more about learning). Synthesizing all experiences give me acceptance and understanding of who I really am.

Learn the skills, mental models, and philosophies of new people

Without a job, I was more willing to spend time meeting new people on LinkedIn. And I met a bunch of WHOLESOME people. I wanted to try out the education industry because I loved learning, so I connected with education founders from BoldVoice, Toko, and Twinoaks. But I also talked to people from some other bigger companies, such as Palantir, Google, and Figma. Though it might not lead to jobs, it helped me understand the current industry and meet cool new people. I was happy to expand my network. This idea was aligned with a core concept from the book, In The Defining Decade: Why Your Twenties Matter — And How to Make the Most of Them Now, that weak network ties will be important assets for future careers.

On Linkedin networking, I didn't always get responses, but when I did, the reward was fruitful. Grammarly's Tech Lead showered me with the knowledge to improve my capstone. Twinoaks's founder encourages me to think of my future self and vision, restructuring my mental model on the relationship between career and life. Grammarly & Notion data scientist doesn't only share his experience and gives me a healthy mindset on“my first job doesn’t need to be perfect.” He has also been in a tough position, and he told me getting your first step in the door is always the most difficult.

With a taste of success, I started to connect with more senior professionals and got to chat with them. Nowadays, I feel very excited to make genuine connections through networking.

Learn how to network in the way I feel happy

My interpretation of networking also changed over time. I realized the best way of networking for me, is not just looking for a job or a referral but really learning about their skills, mental models, and philosophies. Getting a job is important, but feeling connected or inspired by a new person is beautiful. To achieve it, I learned how to find people and asked specific questions that were related to my interests. Hence, my questions became more intentional, tailoring to professionals' life experiences and backgrounds.

This is a sample email to showcase our common background and my dedication to showcasing my burning interest.

It also allows me to solidify my previous network. I met lots of data scientists during this SF summer vacation. I reconnect with them and learn about their jobs and companies. I feel happy to learn about people beyond social settings. I feel quite happy to get to know more about their different angles of them. 😌

Learn how to develop career development rituals

I use career development rituals rather than job search rituals because career development includes much more than a job search. During the career pondering process, I create five pillars of my career development.

I call it rituals because I realize career development shouldn't be a periodical panic but a tight integration with life. Most people put most of their energy into the Apply pillar, and life feels stressful, tedious, and miserable. But when building, I'm passionate and committed. When searching, I'm playful and curious. When connecting, I'm surprised and inspired. When reflecting, I'm peaceful and directed.

Role model as existing solution analysis

Role model analysis as an existing solution analysis is a way that I find helpful to navigate my career. It is because role models are people who are similar to my current self or possess characteristics that I envision for my future self. Best of all, they have already been through the confusing process that I can learn and proxy my future self from them.

To do a good existing solution analysis, I try to understand

The gap between the initial state and the goal state
The similarity and differences between the model solution and my problem

The similarity between my role model and me is the following.

Deeply care about our own learning. We want to be educated.
Have strong confidence in ourselves. We have a clear vision of our future selves even though the job position and industry might not be obvious.
Strategy is ingrained in our belief system.

The difference between my role model and me

I'm more technology-focused
I need to have more space for fun and creativity

Based on the similarity and differences, I will hypothesize my future self will have education-related projects or institutions, but with a sparkle of technology (maybe an ed-tech product). I will probably want to have funny/random components inside my work (e.g., ask my students to survive in Amazon for a month).

Actions after learning: Revisions on my job search strategy

I stopped my massive random application after talking to my role models. I realized I had made several mistakes.

When I panic, I get too caught up in optimizing by local maximum, so I apply without thinking enough about how it relates to my global optimum.
Though we apply by job positions, I'm more grounded on who I want to be, not what I want to do. I want to apply for the future self I want to see.
I don't need to look to realize my dream now; I want to accumulate enough skills and power before starting.
I don't need to find "the perfect job" for my first job. Most people don't have the perfect one.

Thinking of points 1 and 2, it makes more sense to depict my future self before thinking hard about my position and industry. Therefore, I try to lay out my global and local optimum for my career.

Global Optimum: Imagine my future self

If I start thinking of who I want to be in the next 2–5 years. I envisioned the next 2 -5 years; I would be a builder. Like a bodybuilder, I build my skillset muscle every day.

The vision of Esther's 2–5 year future self:

I want to be financially independent.
I want to develop some expertise (data science, strategy, engineering) to have a personal moat.
I want to have a confident, warm, and funny personality.
I want to build up my community.
I want to have enough social and monetary power to build my education project.

From the description of global optimum, I start to understand what I should look for after graduation.

Local Optimum: career decision after graduation

Based on my global optimum, I lay out several ideas for corresponding local optimums.

The place to work: the US, even though I don't like it. But I prefer not to be in Taiwan for financial independence and developing expertise global optimum.
Job positions: To cultivate expertise, I don't want to be a business analyst who does visualization and cannot make any decisions. I want to go to places like Meta where data scientists can strategize product directions. More technical positions like data scientists (not data analysts) or domain experts (like product analysts) will be preferable. As for company products, I will like to work on products that can be applied in education (e.g., Metaverse, or other edtech products) or a product that is on building up a system (e.g., Palantir)
Industries: Tech industries will be my topic choice to gain social power and financial independence. Edtech startups are good for building my future educational project too. The minimum requirement is to get into a growing industry.
Friends: I want to be surrounded by people who have a builder mindset. My current Minerva friends have a strong growth mindset, and I want to find more (the SF tech party is a good place to start).
Colleagues: Builder mindset first. e.g., people like my Meta mentor will do weekend side projects. Curious people second. e.g., people who are curious about new technology. Funny people third. e.g., People who make everyone laugh in the meeting room.
Network: I want to meet more people, like the founder of Twinoak, so that I can learn more from warm, smart, and confident people.
Companies: Based on all my reflections, my top tier would be Palantir, Meta, Chan Zuckerberg Initiative, and Edtech (Duolingo, Lingoda, Udacity, Codecademy, Coursera). My second tier would be other big techs(Airbnb, Uber, Amazon, Google), Notion, Figma, Minerva, and Grammarly.

In addition, Grammarly & Notion data scientists also encouraged me to think from a data science perspective. He broke down data science into five categories and ask me, "what skillset do you want to cultivate for the next five years in data science?"

Data engineering: get the data into the data warehouse
Analytics engineering: operations in the data warehouse
Analytics
Machine learning & statistical modeling
Data visualization

I want to continue with analytics, branching out to machine learning and engineering. It gives me more hints on what other companies might be also suitable for me, and I'm currently still in the process of figuring it out.

A List of career questions

These are the questions that I think of during the process. I note it down to share with you all and my future self.

On myself

Why do I like the company? Is it because of the benefits or the learning? If I ought to sacrifice, what will I choose?
Who do I want to be in 5 years?
What system do I want to build in 5 years?
What would I imagine how a passion project look like building for 5 years?
What do you want to do with the data skills that you learn?

On talking to senior people who are ahead of me

What are the similarities and differences between them and me? Are they good proxies for me?
What drives them to make a specific career choice? Is it the same driver as me?
What's their philosophy on making career decisions? What is their global and local maximum, and how do they connect?

On job search

Where to live and why? Who to live, work, and be friends with?
What company, position, product, and industry?
What is a definite no in my job search for me?
Among the 5 categories (data engineering, analytics engineering, analytics, ML, data visualization), which skillsets do you want to cultivate?

I haven't gotten a job and don't even have an interview at this point. It might sound weird, but I will tell you that I feel truly grateful. I feel grateful to sharpen my visions of my future self and synthesis my self-knowledge over recent years. I also feel thankful to have a peek at the wisdom of people from all walks of life.