Rudy's Rudymentary Analysis
. . . of the Data Science Market
Who's Rudy?
Before the dawn of time...
Alright then, Meet Rudy
Rudy is happy. He is happy because he believes he has found The One.
The One is perfect.. lives in a city he has always dreamt of living.
..whose requirements he can happily meet.
Yup, that's right.
The One has all the qualities he's been looking for his entire life.
He believes he has found the one.... job that felt like a match made in heaven.
He tailors his resume, crafts the perfect cover letter and applies for the job.
Heart broken after the rejection from the one, Rudy decides to take a while to mend his broken heart and a couple weeks later summons the courage to put himself out there again. "Life has to move on" he tells himself.
He searches for the same job title at a different company which piques his interest again,
Adventure seeking Rudy looks at the job description of an. . .
ExcavateAI Treasure Hunting|Mining company:
- Fluent in Latin, Sanskrit and Mandarin.
- Must have deep expertise in Aztec, Mesopotamian, Egyptian and Indus valley civilization to help fine-tune computer vision model to help excavate new sites for ancient treasures while also making new ML discoveries/inventions like Ilya Sutskever.
- Walk office dog to the park.
- Write SQL queries while benchpressing 300.
And the story repeats. . .
At this point, a lot of "the ones" have now broken his heart... and he is willing to bet money that if you’ve not blindly applied to thousands of companies for a data related position hoping for a volume based luck then these are the ambivalency that arise with the exercise of browsing different requirements for the same title:
SMASH the F#@%ing laptop
Am I dumb? God, please just tell me already!
You know what, I'm meant for a spiritual life.
I'll die starving, I just feel it in my gut
Boy, oh boy, is A.I around?
The Zen Rudy
Thus began Rudy’s journey to figure out who he was as an employee. Rudy meditated, self-reflected, reconnected with himself, learned to admire what life has to offer, ahem… ate the sandwich (if you understand How I met your mother reference) and somewhat came with a partial truth whilst learning where he could fit better: All the data roles he saw online if broken down to ground truths and reconstructed back up could be construed as
Engineers: To Rudy, engineers are folks with sharp analytical abilities with a knack for understanding ground truths, apply that newly learned truth with their love for craftsmanship and make a tool that dreamily changes the world whilst also satisfying the soul. Rudy thinks they are tinkerers, the builders, the resource managers, the DIY types.
Scientists: Rudy naively believes that scientists are the Zen Contemplatives who would love someone bring them their favorite food and quietly sneak out the door without a hello, how do you do? While they hold a pipe, contemplating an uncharted idea that is likely already debunked by former giants but know in their hearts is the right path forward.
But Rudy also knows there's an overlap between an engineering mindset and a purely scientific one and the best analogy he can think of are that of experimental physicists, who know how to validate/disprove a hypothesis using experimental designs (which he thinks is somewhat akin to engineering) whilst also having a depth of knowledge in theoretical physics but just doesn't have the time to do them all. "We all have 24 hours, right?".
And so Rudy made a neat little software to help new grads entering the field of data science could navigate with as a beginner so that they wouldn't have to struggle with the confusion he had faced along the way
... because something was off while he searched for jobs. As gullible and impressionable as he was, he forced himself into love the hate he saw some software folks had for bashing javascript for being slow and resulting in spaghetti code.
Or why a ReLU activation function served the data and the objective better than a sigmoid function.
Rudy desperately wanted to fit in.
Rudy's odd fascination with K-means clustering and Linear regression came in handy which helped him understand some of the key differences between Statistics and Machine Learning (his question pertaining to the difference between linear regression in machine learning and statistics?) But as time passed by and a few weeks became a few months, he, yet again, felt a certain sense of unease. He could think of the Edisons and the Einsteins of the generations but somehow not fully classify himself as an engineer (data or software, he contemplated electrical but nah! Time to learn hardware had passed him by, Rudy is dramatic) or a scientist (data or computer) because he also had an itch for...
Enter:
Business was a long lost love of his for he couldn't fully classify himself as an entrepreneur either a while back but this time.. Rudy read books by Brad Stone, "The Upstarts.." and learned the value of teamwork (among Brian Chesky, Joe Gebbia and Nathan Blecharczyk), hustle, ideas, perseverance or even shear willpower to bend the rules (Travis Kalanick) to make things happen. He also read "Amazon Unbounded" and learned the value of customer obsession, long term thinking, culture, strategies and the value of making mistakes. He also read Elon Musk's biography by Walter Issacson and learned the importance of a maniacal sense of urgency among other important things. He then read "The Lean Startup" by Eric Ries and learned the value of MVP, the value of pivoting, the value of failing fast and the value of learning from failure. He then read "The Innovator's Dilemma" by Clayton Christensen (ok he just saw the Google Talk on it)
and learned the value of disruptive innovation, the value of being a disruptor and more importantly the different stages of a company.
- Startup Stage (Rudy's Favorite, high chance of failure)
- Growth Stage (The right company could make you rich beyond your wildest dreams)
- Maturity Stage (fat paychecks to employees) and
- Decline Stage (Rudy suggests never to apply here.)
Business: SO now, Rudy naively thinks of business folks as someone who can sell ice to Eskimos, folks who can make a business out of a business that makes businesses.
And so finally.. Rudy feels like he belongs at the intersection of Engineering, Analytics and Business even for an entry-level job.
Rudy also knows as the career progresses job descriptions start to overlap, he knows to be a CTO one must have engineering, scientific and Business knowledge.
- Rudy is smart. Be like Rudy.
Try the Interactive Title Searcher
[Press Anywhere. One or more buttons]
