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In the course of our lives, we all face the significant challenge of aspiring to that ideal job—one that not only satisfies our professional ambitions but also resonates deeply and authentically with our individual interests and passions. On this journey towards professional fulfillment, we wish for each step to be guided by proven strategies and pragmatic advice that can enhance our chances of success.
I'll begin by sharing my personal story of how I landed a job in an investment fund and then provide you with some recommendations to optimize your job search, making it easier for companies to notice you and stand out among thousands of applicants.
It all started in 2017 when I began my studies in physics. There was nothing in the world that fascinated me more than solving complex problems. It was always an internal satisfaction to comprehend and explain how a complex system could be broken down into smaller parts and eventually solved. While I had some experience in programming due to my interest in robotics, where I had success and won many robotics competitions, my life took a turn when I discovered Bitcoin.
Spending a week delving into what Bitcoin was, reading the whitepaper from the beginning, analyzing charts, I couldn't resist investing a portion of my savings in Bitcoin. My obsession with explaining complex systems led me to analyze and interpret the price movements. I discovered that the exchange I was using at the time, Bitso, had an API that allowed calling price data to Python. However, they didn't provide historical candle data. So, I sought another data provider and started trading with technical indicators—Bollinger Bands, RSI, Williams %R, Momentum, an endless list of indicators. But back then, I didn't apply any optimization method to find the best combinations, nor did I know how to conduct error tests and Monte Carlo simulations.
Throughout my university journey, I underwent significant development in my scientific maturity. This gradual growth not only reflected a deeper understanding of theoretical concepts but also translated into a remarkable improvement in the practical application of advanced methods and systems.
My trading systems began to be predominantly governed by rigorous mathematical models, grounded in exhaustive analysis and meticulous optimization techniques. The implementation of statistical tests became a standard practice in my operations, providing solid empirical support for every decision made.
I also dedicated myself to honing my skills in backtesting, a process that allowed me to validate the effectiveness of my strategies by evaluating their performance on historical data. This not only increased the accuracy of my predictions but also facilitated the identification and correction of possible errors before applying any strategy in real scenarios.
Each of these stages, from adopting optimization methods to meticulous backtesting, has been instrumental in building a robust and reliable trading system. This continuous process of learning and improvement has equipped me with the necessary tools to face the challenges presented by the dynamic world of trading with a solid foundation and a scientifically informed approach.
Fast forward to a few years ago, a period I remember with great nostalgia. It was a time in my life where I learned the most, had many Eureka moments, and enjoyed the process of learning. During this time, I met an excellent friend, Tudor Barbulescu, through his YouTube channel. We connected quickly as we chatted for hours in his Telegram group, where I reported everything I found. Tudor was starting an open-source algorithmic trading library with Python called pyjuque, and I collaborated by implementing the backtesting engine for that library.
Later on, Tudor's project gained popularity and reached the ears of the company where I currently work. It didn't take long for them to hire Tudor, and a few months later, they hired me, all thanks to my friend's recommendation. The rest is history. A few months later, the company let go of Tudor, but he's now working at another investment fund called Apollo Capital. Years later, I met him in Singapore.
None of what I've experienced so far would have been possible without Tudor giving me the opportunity to recommend me, and everything leads to the first point in getting your dream job.
**YOUR NETWORK.**
The job you've always wanted, as in my case, may be within reach through a friend, or you might even find a job for your friends. After I joined, I gave an opportunity to a friend named Gabriel, and he, in turn, gave opportunities to many other people to work in another company based in Singapore. This is a virtuous circle that you never know how it can benefit people. So always be kind and try to give your best to the world because the world will one day repay you in abundance.
This brings me to my next point. With this entire story, we can extract crucial points that led my friend Tudor to secure two jobs in investment funds, and I believe this is applicable to many other jobs in data science.
Tudor didn't wait to be recognized by a university degree to start standing out in his field. Instead, he took the initiative and created an open-source library, inviting everyone to view and contribute to it. Additionally, he used his YouTube channel to keep the public informed about his progress and the latest implementations. This proactive approach not only allowed him to demonstrate his skill and dedication but also built something tangible and valuable to present to potential employers.
Believe me when I say that bringing evidence of collaboration on prominent libraries or even creating a recognized Python package to a job interview can have a much more significant impact than a university certificate. It shows that you are not only capable of initiating and seeing a project through to its conclusion, but you have overcome multiple challenges and gained real practical experience in solving complex problems.
However, it is not necessary to reinvent the wheel to stand out. The essential thing is to have a solid portfolio that demonstrates your ability to execute and complete projects successfully. This portfolio will be your best introduction, leaving a lasting and positive impression on companies interested in your profile.
Another important point is that sometimes employers, including HR, don't do a good job selecting personnel. Many times, they don't understand the value of our work and effort. So, I recommend that if you are interested in a specific position, research who would be your direct boss and send a friendly message via LinkedIn expressing your interest in the position and highlighting your experience in it. This can open doors much faster to companies and even bypass test exams.
But before you can build this portfolio, it is essential to acquire the right knowledge. This is where Quant Academy plays a crucial role, providing you with the tools and instruction needed to embark on your journey in confidently and competently developing data science projects.
**Navegando hacia el trabajo de sus suenos: estrategias comprobadas**
