I got 7 job offers during the worst job market in history. Here’s the data......

Introduction

I got seven data science job offers during the worst job market in U.S history. I’ve completed over a hundred data science interviews, ranging from giants like Apple, Google, Facebook to four person startups. No, I don’t have a Masters or PhD. 

Sometimes, my imposter syndrome asks me what the hell these companies are thinking. I don’t deserve it. Other times, I quietly whisper in my ear that I’m superman and proceed to flex my chest muscles, ripping my shirt to shreds.

Through the process, as a data scientist, I hand-tracked all my data so I could play moneyball with the numbers. Here's what I found.......

High Level Statistics

This table maps out the interview funnel. The white rows, count and conversion, are my numbers. You can see I converted 46% of my onsites to offers and converted about 41% of my applications to phone screens.

The gray portion is the expectation. The expectation acts as a baseline to compare my performance. Stages that dip below the expectation is where I need to improve. You can see, that I beat the expectation at each stage. 

To compute the "expectation", I start with the number of offers I want to target, two. Then, work backwards to figure out what conversion rates I need to target on each stage to hit my desired offer amount.

Let’s dig a bit deeper into those first stages: Applications & Phone Screens. 

How much do years of experience impact my likelihood of getting an interview?

Intuitively, we know that more years of experience should improve our ability to get interviews. But by how much?

I defined interview rate as # of phone screens/# of applications. Basically, how likely was I going to get an interview after I applied to the position?

When I had zero years of experience, my interview rate was 17%. With three years of experience, it was 41%. Each year of experience had increased my interview rate by about 11.5%. Keep in mind, the economy didn't suck three years ago, so I suspect my interview rate would’ve been much higher during a great economy. 

So for anyone just entering a field and is struggling to get interviews, just know that it will get easier as you accumulate more experience. If years of experience matter, the group that’s struggling the most are the entry level applicants. So how many applications should they target?

How many applications should I be sending out to get an offer?

To answer this questions, I developed a new metric called Applications Per Offer or APO. You'll see here, that with 0 years of experience, it took me about 62.5 applications to generate each offer. Compare this to three years of experience, it took me 10 applications to generate each offer.

However, 10 applications per offer is ridiculously low and should not be interpreted as “Oh, I only need to send 10 applications to get one offer.” I’m making a false assumption of the data:

The problem with this metric, is that offers are not independent. Once you get your first offer, your chances of getting a second offer dramatically increase. 

The goal should be getting enough applications in to get to that first offer. Once that first offer rolls in, then we hit a tipping point where other offers start rolling in. Relationship looks more like this:

Unfortunately, I didn’t track timestamps so this curve is based off an eyeball, rather than an actual fit of the data. *Kicking myself…* If we do a rough eyeball of this graph, we see that the first offer hits at around application 50 ish which equates to about 70 to 80% of the total.

So how many applications should you target? As a ROUGH approximation:

1. Figure out the total number of applications you’ll need to send to hit your target offer amount.

2. Multiple that by 80% to get the required apps until first offer.

if you have 0 YOE, let's say you use my 62.5 Applications Per Offer metric. If you wanted to target 2 offers, you'd need 62.5 x 2 = 125 applications. 125 x 80% = 100 applications. Expect to send at least 100 applications to hit that first offer. More, when there’s a poor job market.

Caveats:

• 62.5 Applications Per Offer is based off my own data when the economy was good. It’s likely way, way higher when during a tough market. Later in this post, I talk about how to adjust for economic downturns.

• Every person's situation is different. I know people who've gotten an offer sending only a few applications and some who've sent hundreds of applications with no offers. There is high variance here.

One additional problem to point out, is we’re treating every application the same. In reality there now. Let’s figure out if we can improve our odds with a specific application strategy.

What's the most effective method of applying to positions?

If you're going to aim for 100+ applications, not every application should be treated equally. We intuitively know dropping your resume into the job board blackhole has a terrible response rate. But by how much? Let's look at the numbers.

We’ll use interview rate again (# of phone screens/# of applications) as our core metric. Unfortunately, I didn't record my data for years of experience = 0, so I only have data for the YOE = 1 and YOE = 3. But let’s first assume the interview rates across different methods of applications:

You can see here that the interview rate is around 43% for referrals, 29% for cold e-mails and 6% for direct. If you have 0 YOE, take a haircut off these numbers. Cold e-mail is finding the hiring manager's email address and emailing them your resume. I'll talk about this in a future post.

This graph says that yes referrals are the best channel. However, what happens when we have three years of experience?

With three years of experience, you'll see that across the board, my response rates are higher. But looking at the “Direct” channel, we see a dramatic increase in my interview rates. At zero years of experience, cold e-mails increased my odds by nearly 5x compared to direct. However, at three years of experience, cold emails only increased my odds by 1.3x, which is good, but not amazing.

This leads to the insight that the more experience you have, the better of a response you’ll get through direct applications. With more experience, you have more leeway to go the easy route.

So how should I spend my time?

Figuring out how to spend your time is an optimization problem. You want minimize the amount of time you spend to maximize your ROI (offers). We all know that getting referrals is the best strategy. For a referral, the cost to yourself is <5 minutes writing a message asking for the referral. In return, you get an interview 43% of the time.

If you know someone at the company, even as an acquaintance, it's almost always worth it to ask for the referral.

However, if we don't know anyone at the company, this is where it gets interesting. If I had a choice of either applying directly or through cold e-mails, what is my ROI for each channel? To do this, I built out a simple model:

Assumptions:

• Response Rate to Direct Applications: 2%

• Response Rate to Cold E-mails: 10%

• Probability of Offer given Phone Screen = 12.5%

• Direct Applications take 15 minutes to complete

• Cold Emails take 45 minutes to complete

Pure Direct is purely applying only through direct job boards. Pure email is finding key decision makers at the organization and sending a cold e-mail pitching yourself. 

If we applied 100% of a single strategy, using our simple model, here are the results:

With zero years of experience, writing cold e-mails has the best return on time spent. However, this changes when you have more experience, as the response rate to your direct applications increases. Now, if we were to update our assumptions with my actual response rates with 3 YOE:

You can see here that although cold emails require fewer applications, it requires significantly more time. This leads us to say that, as you accumulate more experience, if your goal is to maximize offer amounts, cold emails may be less and less optimal. 

However, we know that cold emails have a higher response rate. Since they require more time, if you’re more experienced, save cold emails for the companies you love. 

Note: This is a simplistic model that used basic algebra to make these predictions:

def compute_required_hours(time_per_app, response_rate, interview_offer_rate):
    expected = 1
    num_apps = expected/(response_rate * interview_offer_rate)
    
    hours_required = (num_apps * time_per_app)/60
    return hours_required, num_apps

I made it to the onsite. What are my chances of getting an offer?

Let's use some basic probability to figure out your chances of getting the offer.

You've made it to the onsite. Congratulations! Let’s assume, that the onsite has 4 interviews, ranging from behavioral to technical. Typically, you'll need at least 4 yes' to get an offer. However, a single No will result in a rejection.

To make computations simple, let's assume that I knew the right answer to a question 80% of the time and I didn't know 20% of the time. If I knew the right answer, I'd receive a Yes. If I didn't, I'd receive a No.

So with an 80% chance of passing a single interview, the probability that I pass all interviews is 0.80^4, which is 40% chance of getting an offer. Therefore, we'd expect 0.40 offers. To hit one offer, we’d need 2.5 onsites. (2.5 onsites x 40% = 1 offer). 

While this isn't an exact science, even if you're well-prepared, with a probability of 80% of passing each interview, your chances are still worse than 50/50 in getting an offer for a single onsite.

Now, let's throw in another variable: competition. During covid(or some other economic downturn), companies cut headcount so they only have 1 spot available. Now, let's say that you got 4 Yes' but two other candidates got 4 Yes'.

At this point, it turns into a game of chance between you and the other candidates. Since our previous expectation calculation is assuming that 4 Yes’ = an offer, we must normalize our expected offers by the number of competing candidates who also got 4 Yes’. 

So in our example, our expected offers goes from 0.40 to 0.40/3 = 0.13. So if you originally needed 2.5 onsites to get 1 offer, you will now need 7.69 onsites to get one offer. 

In simple terms, if its an ultra-competitive market (like during covid), expect to do 2 to 3x more onsites than usual. I always ask the recruiter how much headcount they have to get my odds. In scenarios with multiple headcount, no need to normalize.

So what’s the best strategy? To increase the total number of offers, your levers are:

1. Raise my probability of passing

2. Complete more onsites

If I raised my probability of passing from 80% to 90%, for a single onsite, this increases my probability of receiving an offer from 40% to 65%. 65% isn't too shabby but can we do better?

However, let's say rather than 1 onsite, we increase our number of onsites to five onsites. Assuming we still have 80% chance of passing, so 40% chance of an offer for a single onsite. This means we have a 60% chance of failing an onsite. The probability that we fail all five onsites is only 7%, which means you have a 93% chance of receiving 1 or more offers.

It’s better to take 40% chance of success five times versus 65% chance of success once. So the insight here is that first, get good enough. Once you’re good enough, your time is generally better spent getting more onsites than raising your probability of passing, unless it’s a company you love.

It’s not worth it to spend 2 hours studying(to improve from 80 to 85%) some obscure thing a company might ask you, compared to getting more shots on goal. 

Takeaways

In the case of job recruiting, it's better to be data-informed than data-driven. There's a lot of nuance in job recruitment. There are some companies you like more and some companies you don't care much about. There are factors such as the economy, job fit, competition, resume, interview skills that result in an offer.

So my recommendations based off the data and personal experience would be:

• If you have a connection for a referral, always ask for it. You have the best odds, even if there’s a chance your connection doesn’t respond to you. 

• If you don't have much relevant experience, your strategy should lean cold e-mails over direct applications. But never  100% direct apps or 100% cold e-mails strategy. Always have at least a small percentage in one channel as an exploration mechanism. 

• If you are on H1-B and have a time limit to find a job, maximizing the number of offers per unit of time spent is the ideal strategy:

  • With fewer YOE, the best strategy is to lean more cold e-mails, referrals than direct applications. If you need a rule of thumb, always go referral. Afterwards, go 80% cold e-mails, 20% direct applications. 

  • With more YOE, you can afford to send more direct applications and just reserve cold emails to companies you love. 

• Be dynamic in your strategy. Don’t rigidly stick to 20% cold e-mails or any type of hard rule. Always gauge the response and the market and dynamically update your strategy. Everybody has a different situation. 

• For companies you love but have no referral, always at least send a cold email. The response rates are still higher than direct applications.

• One area I didn't mention, was if a recruiter reached out to you. If it's a recruiter from the actual company, I'd say take the call. Since they made the move, you're in a position of leverage and you don't have to do any work and your interview rate is close to 1 (compared to 43% referral, 29% cold e-mail etc).

• Your odds are better if you increase the number of onsites, rather than increase your probability of passing. 

Anyways, I hope this post was an insightful look into the job recruitment process. Best of luck and feel free to reach out with any questions! Feel free to reach out on Twitter.

The Four-Step Formula to Solve Any Problem

Fuck. Fibonacci Sequence? I hadn’t heard of that since high school. I didn’t remember what that meant. I didn’t have an analytical degree in college and now I was being tested on it in the interview. I felt stupid.

I botched the interview, using a buggy, inefficient solution. It wasn’t until years later that I learned that fibonacci sequence is standard topic you learn in any data structures & algorithms course. I felt stupid.

But in the case of that interview, if I had just taken a class on data structures, I would have known there are multiple solutions: recursion, memoization or python generators. Failing the interview didn’t mean I wasn’t smart. It meant, I just didn’t take the class yet.

When we see entrepreneurs, engineers solve problems quickly, we assume genius. Richard Feynman has an amazing story of how he beat a man with an abacus using mental math. The reality is, he happened to know a cubic foot contains 1728 inches. The reality is, he happened to know a cubic foot contains 1728 inches because he’s seen so many math problems. 

Solve more problems and you start building a critical mass of problems & solutions. Genius is pattern recognition.

If genius problem-solving is pattern recognition, what does a pattern look like? While, business problems, engineering problems, personal problems may seem different, the problem-solving process is the same in all disparate fields. Yes, you can train “genius” or “intelligence.” 

In 1945, famous mathematician George Polyar published his four step formula toward problem-solving. Here’s the formula:

1. Understanding the Problem

“The formulation of the problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill.”
― Albert Einstein

Failing to understand the problem, is like constructing a puzzle without even looking at the final picture. This is the most important step, which is why I spent more time on this section. Understanding the problem, can be broken down into multiple components:

Data

What information or data do we know to be true about this problem? In math, this would include the definition of the relevant variables. In product, this is actually talking to the users to figure out what they’re struggling with. In business, this is understanding what factors are driving a loss in profit.

Conditions

What condition must I follow within this problem? In computer science, this might mean that your solution must be in linear time. In business, this might mean your solution must be under budget. In design, this might mean your design is limited to 3 different colors.

Assumptions

What assumptions am I making about this problem and can I validate the accuracy of these assumptions?

Every problem, sentence, question, has an inherent assumption behind it. For example, a common business problem is falling profit margin. If we were to solve this problem of a falling profit margin, we are making the assumption that the company cares about profit margin. This assumption could be wrong, in that the company cares about growth, not profitability.

Unknown

What am I trying to solve for? What is unknown?

In algebra, this is the variable we’re aiming to solve for: 10 = 3x + 4. In programming, this is the program we’re aiming to write. In business, this is the strategy or project we’re trying to come up with.

• Is this problem worth working on? What’s the benefit of solving this problem?

• How does the formulation of the problem affect our approach? How does the formulation affect our result?

2. Devising a Solution

Devising a solution is more of an art than a science. At the core, devising a solution comes from our ability to break down the problem into smaller components and using our existing knowledge to find an optimal solution. With experience and proper understanding of the problem, we can recognize components of other solutions that can apply to this problem.

Here are some questions you can use to find the solution:

To find solution(s):

• Can I break this problem into smaller sub-problems?

• Is there a simpler version of this problem that I can solve?

• Are there any related problems that we have optimal solutions for?

• Are there any solutions I can eliminate?

• Can I invert the problem? What would that look like?

• Is there a bottleneck?

• Can I draw this solution out?

• Is there a pattern? What is the pattern?

To optimize the solution:

• What is the most important component(s) this solution should address?

• Is there a simpler version of this solution?

• What are the edge cases of this solution?

• Where can this solution go wrong? What are the risks?

• What would happen at the extremes of this solution?

3. Executing the Solution

Once you have the solution mapped out, then it comes down to execution. We frequently realize mistakes in our solutions after execution, which means we go back to the devising stage. The most important component is to continuously validate whether this is the right step or not.

4. Reviewing your results

Once the solution is executed, our immediate response is to validate our results. The core principle here, is to figure out:

1. Whether this solution works properly.

2. Whether this is the best solution.

3. Other problems this solution can apply to.

Questions you can use to dig into these components:

• Is this the right solution? How can I validate our solution?

• What scenarios would invalidate this solution?

• Does this solution generalize well to other problems? If so, which problems?

• Are there simpler solutions that can give us the same result?

• How will this solution be used?

Not knowing the solution doesn’t mean you’re dumb. It means you didn’t have the requisite tools to solve the problem. For experienced problem-solvers, you’ll probably see that you already follow this process. For inexperienced problem-solvers, this post should outline a framework to tackle most problems, whether you’re in finance, engineering, data science or business.

But regardless of your field, remember that genius is pattern recognition. Remember, It’s not the number of hours you spend solving problems, but the number of problems you solve.

2019 Annual Review

Now that we're two weeks into 2020, the "New Year New Me" motivation wave has crashed. Gyms are no longer crowded. Many have forgotten their new year goals and continue with their merry ways.

Perfect time to publish the annual review post.

During every year review, we cycle through our memories, reliving that amazing adventure we had through the jungles of Thailand, that amazing conversation we had with that stranger on the subway, or that energizing feeling of crushing the work project you've toiled away at.

As I reflected on my favorite moments, books, tweets, movies of 2019, I noticed a single theme: determination. From reading about Alex Honnold's free solo ascent up to El Capitan, to Jose Alvarenga surviving 438 days in the sea, to my own bike trip from San Francisco to LA, everything that caught my eye this year resembled some form of determination.

In 2018, my theme was learning. 2019, it was determination. My theme for 2020? Let’s find out.

To find out what's in store for 2020, I conducted an annual review in this order:

1. Biggest Wins

2. Things to Improve

3. Things to do more of/less of

4. 2020 Goals

5. Habits

6. Memories

Biggest Wins

Biking to LA: Last January, a few friends and I set a goal to bike from San Francisco to Los Angeles. We didn't bike regularly, but we were committed. During this challenge, we overcame a excruciating back pain, patellar tendonitis, 21,000 feet of elevation, 506 miles to complete the challenge.

Career Skill Development: 2019 was the biggest year of growth for my data science career. I've only been working in the field for two years. I also didn't study computer science in college which meant my engineering skills had significant room for improvement. I learned how to write packages, unit tests, build end-to-end machine learning solutions. I also learned how to effectively deal with stakeholders, lead and influence.

Dropping 4% body fat in 1.5 months: In June, I set a goal to get to 15% bodyfat. I crushed it. I started at 17.6% and dropped to 13.4%, while gaining a 1lb of muscle, in about 1.5 months. The recipe for this, was cooking spaghetti squash at home, exercising everyday, and 80% vegetable + 20% meat plates. Oh and biking 500 miles helped.

40 total dates: In 2019, I set an intention to find a long term relationship by dating a lot. I succeeded in the second part. I went on 40 dates over the course of 2019. I made a lot of mistakes and embarrassed myself. I got rejected a lot. I learned to keep first dates cheap and quick. I learned to apply less logic and more intuition. I learned I need to communicate better. Someone can check all the boxes, but just doesn't feel right. But most importantly, I learned what I valued in an ideal women.

Things to Improve

Technical Craft: I don't have a PhD. I don't have a Masters. Being on a team of PhD's, imposter syndrome stands in front of me everyday. But rather than let it crush me, I use it as fuel to become better than ever. While my technical skills aren’t terrible, I'm holding myself to a higher standard. To be completely honest with myself, compared to my teammates, my technical skills are definitely on the weaker end.

Dating/Relationships: While I did go on 40 dates last year, I didn't accomplish my goal: find a long-term compatible, relationship. I went hard on dating for the first eight months. For the last few months of the year, I stopped dating as frequently since I was burnt out. I’ll need to put myself in situations where I can meet women who have aligned values.

Leadership/Influence: At work, my main focus has been on execution and less on leadership side. A focus for next year, will be on how to have my ideas be heard, effectively sell them and be able to positively influence the organization. This also means thinking from first principles to challenge ideas before accepting them. The first step, is to flawlessly execute. This builds trust and creates political capital to have influence.

Things to Do More Of:

What were the experiences that triggered the highest number of flow states in 2019? What were the experiences where I lost track of time? This is what I need to do more of.

Surfing: I'm a beginner/intermediate-ish surfer. Whenever I catch a wave while surfing, the ocean drowns away all my worries as it pushes me to shore. It's ecstatic. Surfing is the on activity I must do when I’m feeling burnt out. There’s something magical about being out in the ocean, no technology, just you and mother nature.

Cycling: If you want to train “determination”, I recommend cycling. The repetitiveness of cycling turns it into a meditative sport. The repetition forces you to calm your mind, pay attention and most importantly, to just keep going. Although this year’s theme won’t be determination, I’ll need to continue applying determination to my newer goals.

Meaningful conversations: The most meaningful conversations are the ones that touch our hopes, fears, dreams. They shine light on our imperfections with the other person listening intently, absorbing every word and emotion floating in the atmosphere. Whenever I have amazing conversations, I leave them feeling energized.

Data Science: Over the last few months, I've been enjoying data science more than ever. The process of thinking deeply about a problem, finding a solution and seeing results triggers an odd, light buzz throughout my body. Constantly grinding, then solving a problem, makes me want to jump on a table and pound my chest, giving a Tarzan-like scream.

Writing: I didn't write as much as I wanted this year. I wrote a giant post about my bike trip but stopped right after. Not only is writing a great flow activity, but it's also a great way for me to hone my thinking and understanding of ideas. But most importantly, writing is an amazing way of sharing what I’ve learned with others.

Things to Do Less Of:

Binge Drinking: I used to party a lot in my college years. This year, I can count on one hand, the number of times I had a hangover. This is great and I plan to keep this up. The times where I notice myself becoming irritable, upset or anxious are usually after nights of heavy drinking. I still plan to have a couple drinks. However, as I get older, binge drinking doesn't really have a place in my life. Unfortunately for my friends who love to binge drink every weekend, this may mean we grow apart.

Ordering Take Out: I habitually order delivery. This isn't great for my wallet and my health. While I don't eat crappy food, ordering out usually means I don't know what's actually going into my food. The solution here is to cook at home more often.

2020 Theme - Craftsmanship

Based off my 2019 reflection, my 2020 theme will be on craftsmanship. Like a blacksmith welding metal, the craftsman pay attention to the minutiae of everything they're building. They pursue their craft with the highest standards of excellence. Excellence means we hold ourselves to a higher bar than others expect of us. You pay attention not just to how it looks on the outside, but the inside. You take feedback but its the only way to improve.

Combine 2018's theme of learning with 2019's theme of determination and we get “craftsmanship”. The principle here is to treat all my projects as a part of my "craft." Not just work, but any personal projects. This is the pursuit of excellence.

Outcomes/Goals

Use data science to generate $XXX of profit for the company: I won't put an exact number publicly, mainly because the number is based off internal information. But, by aligning my goal with the companies, this means any work I do will not only entail using data science, but also understanding the business impact of the project I work on.

Level up as a Data Scientist: While I won't divulge my data scientist leveling, my goal here will be to level up my skills so they hit the next level. This means working on additional projects related to relevant topics outside of work. This may include heterogeneous treatment effects, deep learning, chat bots. Ideally, this goal would build off the first goal. By building valuable projects, I can accomplish two goals at the same time.

Use writing as a method to level up data science skills by publishing 50,000 words and 20,000 views,: I'm a better writer than most data scientists. Combining data science with writing is how I leverage my stronger communication skills. Clear writing is clear thinking. And if i can apply writing to my data science skills, this forces me to clarify my ideas within data science. 50,000 words comes to a 1,000 word post bi-weekly. This would give me enough buffer to also building projects that I can write about.

Find a long-term, compatible relationship: The key here will be to continue taking swings. I'll only need one to accomplish this goal. To do this, I'll also need to put myself in situations with people who have similar values to tackle the compatibility component.

Travel to 5 new cities/countries that I've never been to: I didn't travel much last year. Outside of snowboarding in Japan and biking to LA, I stayed put in the Bay Area. I've traveled quite a bit out of the country, however, I've never been to cities like Chicago, Austin and Seattle. This year, I plan to visit at least 5 new places outside of California. I currently have a trip planned to Sri Lanka.

Habits

Connect w/ people who have similar values: This ties well with my dating goal. The only difference, is that the focus isn't just on meeting women, but meeting people who have similar values. So far, the best place has been through the internet, especially online communities. If I have an obscure interest, I can always count on someone on the internet to have the same interest. How can I find similar communities in real life?

Spend more time in flow sports: surfing, cycling, snowboarding: Sports is my favorite flow activity outside of work. Since my work is extremely analytically focused, it'll be important for me to engage in activities that use my body outside of work. Sports that get me into flow are surfing, cycling and snowboarding. However, sports that I also enjoy include football, basketball, tennis. Do more of this.

Cleanliness: Over the years, I've significantly improved. From our cock-roach infested college apartment 6 years ago and now somewhat maintain a clean room. However, cleaning still feels like a chore. Adopting Marie Kondo's attitude toward seeing cleaning as a way to "spark joy" will be key.

Memories

Favorite Tweet(s): High Agency Behavior

Favorite Article(s): Bus Ticket Theory of Genius

Favorite Song: Trippy by J. Cole

Favorite Video: Mac Lethal performs poem from 84 Year Old Man

Favorite Movie: Free Solo

Biggest Challenge: Biking to Los Angeles on Patellar Tendonitis

Favorite Sports Moment: Klay Thompson draining his free throws after tearing his ACL.

Favorite Book: 438 Days

Favorite Course: The Wim Hof Method

Favorite New Artist: Justice Der

Favorite Quote(s):

It's about being a warrior. It doesn't matter about the cause necessarily. This is your path and you will pursue it with excellence. You face your fear because your goal demands it. That is the god damn warrior spirit. - Alex Honnold

"When you’re told that something is impossible, is that the end of the conversation, or does that start a second dialogue in your mind, how to get around whoever it is that’s just told you that you can’t do something?" - Eric Weinstein

How Three Tech Dudes Biked 500 Miles from SF to LA (Finale)

Pismo Beach, California

If you missed it, here’s part I and part II.

200 miles to go

As I cycled up “The Climb”, The bright blue skies welcomed me to the peak of the hill. I pulled out my Gatorade, chugged it and closed out the hardest climb of this ride.

We ended the day in the small town of Cambria: 71 miles, 5514 feet of climbing, 5000 calories burned.

Six days down. Four days left. Two hundred miles to go. After Big Sur, the three of us leveled up in cycling. Pismo Beach, 45 miles, 2000 feet of elevation? Easy money.

We left at 11am the next day and crushed the first 18 miles at a 14.5 mile per hour pace. (Note: The average pace was around 10 to 12 mph depending on incline). I ran into problems with my gear shifters where I couldn’t shift from my larger gear to my smaller gear. We stopped at Morro Bay to get my gears fixed and munch on Taco Bell.

As we continued along the route, we stopped by a gas station to fuel up and use the bathroom. At each stop, we’d take turns watching each others bike while the other went inside to grab water and snacks. We laid our bikes down and rested on the curb. As I pulled out more topical ointment to rub on my knee, a soft-spoke, old lady, probably in her 60’s approached us.

San Francisco had trained me to ignore strangers. But as she came up to us, she seemed befuddled. An emergency tire light went off in her car. I explained to her that she just needed to fill up her tires. I intended to end the interaction there, but we could see it in her eyes that she needed our help. Raymond, being the great samaritan, stepped in and helped her fill up her tires.

She told us that normally, her husband knew what to do. But he had just passed away, so she was freaking out. I had been battling my knee pain & Raymond had battled his back, but it was important to put things in perspective. It’s easy to forget that we aren’t the center of the world. Everybody is going through their own challenges.

We left the gas station. I was expected to lead the whole trip but this time, I asked Raymond to lead.

Something was wrong.

Like a baby in the middle of the night, the pain in my knee started screaming its way into puberty. The volume on my music was turned on full blast, yet I couldn’t hear it. There was absolutely no way I could bear this much pain for another 200 miles.

I popped a painkiller which would dull the pain for 40 minutes. But like a lion trapped in a den, it returned with hunger and vengeance. I endured four days on this knee, by pressing the ignore button but I could no longer do that. If pain was an education in enlightenment, I guess it was time to start class.

Brian and Raymond cycled off into the distance. They got so far ahead, they were no longer visible. I had about 20 miles left. My mind began thinking of the next four days. 200 miles on this would literally be impossible, but I couldn’t focus on the 200 miles. I had to focus on the next step.

The pain would ignite every time my left leg was at the top of its stroke, bent at a 90 degree angle. At 70 pedals per minute, this meant I’d be bending my leg about 8,400 times until I got to Pismo Beach. I could compensate the pedaling with my right leg, cruise for 10 to 15 seconds then repeat. This could cut down the amount of times I’d bend my leg by about 20-30%. I just needed to make it to Pismo Beach and then I could figure it out from there.

It’s funny. When something is painful or uncomfortable, the mind will start to self-rationalize its way out of the pain. I became fearful that I was causing permanent damage. And this fear, caused me to be more timid and slurped away at my self-confidence.

As I continued cycling, I see black dots turn into actual human beings. Brian and Raymond. They waited for me and we were close. 5 miles out. We made it to Pismo Beach.

The Search

When we got to Pismo beach, I spent hours ferociously Googling the shit out of the words: “bike”,”knee”,”pain.” I stumbled upon various solutions: getting a full custom bike fit, using CBD oil, ice, topical Castor Oil, a stronger knee brace, more pain killers and cryotherapy. I decided I would try all of them.

I spent the night with an ice pack glued to my knee. I spent $20 on an uber to CVS just to get a stronger knee brace. I called every bike shop in Pismo Beach for a bike fit. I called everywhere for CBD oil & cryotherapy. I had to do everything in my control to solve this problem. If I lost, so be it.

The devil in my mind convinced me that I could be permanently damaging my knee. That shot my confidence. The next day, we had a 60 mile ride to Solvang. 45 miles on this knee was fuckin hard. There no way I could do 60 miles on this knee.

With a few strokes on the pedal, I couldn’t do it. I was done.

Ubering was the last resort and I had no other choice. I had to call the $120 Uber to Solvang. Fuck. Fuck. Fuck.

I felt like I was compromising on the challenge. I felt like taking an Uber once gave me an easy out. It would make it easy for me to take an uber the rest of the way till LA. But physically, there was no way I could relive those painful 45 miles again. I had to accept the circumstance. I did everything.

I arrived in Solvang and found a bike shop that re-adjusted my seat. When cycling, pressing the heel of your foot on the pedal, your leg should lock straight. With the ball of your foot on the pedal, the leg should bend at about 30 degrees. We raised my seat up about an inch.

This made a HUGE difference. The pressure was no longer on my tendon, but on my thigh.

Next, I purchased CBD oil from the nearest health market to help treat the inflammation. The closest cryotherapy shop was The Lab in Santa Barbara.

The next day, I left Solvang at around 8am with the hope of making it to the cryotherapy shop by 2pm. I felt the cold breeze of the morning as I cycled past the Solvang vineyards, gingerly pedaling to keep the pain dormant. I pedaled up the ramp of the highway, with cars frequently vrooming by at 60 mph. The shoulder was wide and with each stroke, I pumped more confidence through my legs.

I pedaled for about 7 miles. A giant hill loomed. And then it awoke.

The pain started kicking, screaming, begging me to give up. Breathe. Breathe. Breathe. I’d often start meditating when my knee began hurting but the pain was unbearable. I clutched the brake, pulled over to the side and rubbed some Bengay on my leg. Nope, not happening. I called an Uber for the last 35 miles to Santa Barbara.

When I arrived in Santa Barbara, I immediately went to the cryotherapy shop. I had tried every single item on my list and this was my last hope. I had already Uber’ed two legs of this trip. If this didn’t work, it was game over.

When I got to The Lab, the shop reminded me of a high-tech garage from the future:

The place had a VO2 max monitor, a locker room, astroturf grass with thick ropes. Maximizing human performance. Now this was my type of place!

There were two blonde-haired men standing at the counter, with a slick-back comb over dressed in track suits. I explained to them that I was on a bike trip from SF to LA and that I had patellar tendonitis. He responds by saying Oh, cryotherapy for inflammation injuries. Let’s get you in.

I strip off my clothes, with only my boxers remaining. I put on a robe and slip on a pair of socks & slippers. This is what the chamber looks like:

The trainer explained that I’ll be inside for three minutes. I step into the chamber and the trainer yells out “if it gets too cold, just let me know and I can let you out early.” Fuck that, I’m going the full three minutes.

The chamber starts and liquid nitrogen starts permeating every inch of space within the chamber. The temperature is -275 degrees. I stand and start focusing on my breathe. I could feel my hands and feet start shivering and pretty soon my entire body was numb. It felt as if I was getting stabbed by thousands of needles. I start breathing heavily since shallow breaths would make me feel colder. The temperature dropped to -300 degrees  and then I stepped out.

When your body is brought to extremely low temperatures, the body pushes the blood to its vital organs. But when you step out of the chamber, vasodilation occurs. This means the blood vessels expand wider than normal. This resupplies the body with much richer, nutrient-dense, oxygenated blood, which is especially good for injuries.

The trainer wrapped an ice pack around my knee. As I walked out I asked the trainer if I could cause permanent damage to my knee if I kept biking. He nonchalantly replied that many people come in with patellar tendonitis and assured me I wouldn’t tear anything. In fact, he encouraged me to finish the ride.

Boom. Part of what had been stopping me from biking wasn’t the pain. It was the fear of permanent damage. But because the trainer had said I couldn’t make it any worse, I took the pain I had to endure as a personal challenge. I promised myself that I was going to make up the 35 miles I had missed.

I returned to our hotel, changed back into my biking clothes and clipped onto my bike. I began pedaling. No pain. I took a few more strokes to the stop light. No pain. I pedaled another block. No pain. I pedaled another mile. No pain. 4 hours later, the pain gradually started creeping back, but I had made up the 35 miles I missed.

Call it placebo, but I was back in business. Cryotherapy had worked.

The Home Stretch

By this point both Brian & Raymond were tired, but they were in much better condition than I was. After pushing through Big Sur, each route was relatively flat. Armed with my newfound confidence, I was ready to complete the ride. We had two days left: 45 miles, 1700 feet elevation to Port Hueneme, then 50 miles, 900 feet elevation into Santa Monica.

After cryotherapy, I was able to bike but my left knee felt ginger. I couldn’t exert a ton of force on it, but I could ride at a  relatively consistent pace. I stayed on lower gears and kept my own pace while Raymond, Brian flew ahead of me.

We completed the 45 miles to Port Hueneme and the vicious pain I experienced at Pismo Beach continue to stay asleep. One more day, 50 miles into Santa Monica.

As we pedaled into Santa Monica, the SoCal blasted heat on our yellow jerseys as we pedaled into Santa Monica. I’d turn right, and see ocean blue waves, breaking along the coast, as surfers caught the next break. Beautiful, bikini-clad women, hairy old men, bubbling young kids frolicked across the sandy beaches as we coasted by. Southern California. Malibu.

Malibu was the most dangerous portion of the ride. 50 mph, small bike shoulder with cars double-parked meant we shared the lane with the angry LA drivers. As I glanced at my Apple Watch, I’d saw the mileage tick up. 23, 24. Each tick inching us closer and closer to Santa Monica.

My knee has slight bits of pain but it was manageable. With LA in sight, adrenaline started to kick in, dulling the pain, allowing me to use my left leg to pedal.

Brian felt his tire and felt it falling flat. Probably a hole. But at this point, we didn’t give a f*ck. We tasted victory. So he pumped it up a bit and continued cycling.

The road transformed into a beach and the famous Santa Monica pier got larger and larger. I kept telling myself to pedal faster.

As I pedaled, in the corner of my eye, I see a cute girl.

Nope. I pedal harder and harder as if I didn’t even have patellar tendonitis. At this point, my mind was in this not give a f*ck attitude. I was willing to give up my ability to walk for a few weeks.

Coastal shrubs turned into beautiful mansions. Bright blue skies became tinted with white smog population. Los Angeles. We made it.

Epilogue

Raymond, Brian and I gathered at the Pier and took a final picture:

By the end, here were our statistics:

• 506 miles

• 10 flat tires

• 21,000 feet of elevation

• 21,960 calories burned per person

• 11.8 average miles per hour

It might be easy to think that the three of us were extremely fit. Or that we’re crazy. Or that we’re “outdoorsy.” Brian didn’t have a bike before agreeing to go on this trip. Yet, he cycled the highest number of miles out of all of us.

Your misogi might not be physical. It can be intense, like coding everyday for 365 days, going from zero experience to world championships in public speaking or losing 100 lbs.

But it can also be on a small scale, talking to that guy/girl that’s out of your league, emailing the celebrity, singing your lungs out at the karaoke bar.

Because looking back on our lives, the moments we’re proudest of, are the moments we pushed ourselves to be greater than we ever thought possible. They’re the moments when we said f*ck you to fear and did it anyway. They’re the moments when we crushed our self-imposed limitations.

So step out the door, take a whiff of the beautiful world we call life. Because once you realize the power of the human mind, I promise you, that you’ll never be the same again.

Thanks for reading!