4 Reasons To Work In Tech Firms vs Banks As A Data ScientistNicholas LeongBlockedUnblockFollowFollowingMay 25Photo by Franki Chamaki on UnsplashBe careful of what you wish for.
We all know that data science is the hype nowadays.
Everyone wants to be a data scientist and they throw themselves into it once they get a chance.
If you’re reading this, chances are you are looking/interested in the field and are still, looking for that perfect data science role that may one day ‘Change The World’.
Well, let me let you in on a little secret, it may not be as fun as you think.
Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.
— Chris Lynch, Vertica SystemsAccording to a data science article, almost 20% of data scientists work in the financial industry, while 37% of data scientists are employed in the tech industry.
The financial industry pays well, some of us are drawn to it, but is the financial industry really using the data they have to the fullest ?After getting through your degree/bootcamp/job, it’s time to get your feet wet and decide on opportunities.
If you are ambitious (and desperate) as I was, just trying to put your foot in the door, you would grab any opportunity that may have been presented to you, as long as it had the name data in it.
My job today, is to stop you before it’s too late.
#Hold UpI will be focusing on banks vs tech firms as a data scientist today as I can speak from personal experience.
With the rise of job openings on data related roles everywhere, I am here to share some of my experience, roles, environments and why I think it is better to pursue your career in the tech industry instead of banks as a data professional.
(Disclaimer : I only speak from personal experience , things may differ for everyone.
I also speak for my country only, Malaysia.
I am aware that many foreign banks are making good use of their data.
)You mean the world in tech firmsIt’s true.
Currently, I work in a tech (not to be named) company where we run a site/app to connect people to buy and sell stuff.
I must say, they cherish their data dearly and we (data team) are seen highly as the gate-keepers of data.
Here are a few points at the top of my head :ALL decisions made by each department have to be backed up by some sort of dataData is the main driver for new products / featuresData access is limited to the data team.
Hence, I get to communicate with ALL the other teams to find out what are they doing with the data they requested.
The top management also generally care about data and how it’s used.
Keep in mind that this is extremely important as it determines what kind of tasks you will get.
A data driven company with a data driven management can lead to amazing things like:Think of various ways to monetize the data you haveExplore possibilities on how can you use data to improve their site/product/app (like a data product)Encourage you to provide insights and recommendations in the data they requested, they see this as one of your skillsExplore machine/deep learning with proper use casesFurthermore, I am also responsible in loading data from multiple sources into our database.
I feel like I am literally keeping the world spinning and the company alive as my code is deployed to grab data that is ultimately used at the end of the day for analysis and support.
We once went through a week without loading data into our warehouse and majority of the company felt miserable.
They were still functioning, but they did not have a safety net to backup to.
Photo by Ben White on UnsplashThe opposite can be said for banks.
I have 7 months of experience in 2 different banks.
The first one was an intern position in the data team, the other was a data analyst in the data team (or decision management as they call it).
Note that 1 of the banks I have worked for holds the largest market share in the industry for my country.
In other words, I have worked for one of the largest banks in my country.
For starters, you are not required to provide anything extra on the request of data you received.
Did you manage to find something meaningful ?.An idea ?.Or some kind of recommendation to improve their product ?.They don’t really care.
They only require you for reporting and often, you only report excel numbers which are already automatically generated, you just have to be there to double check it.
They don’t even need graphs, visualization or input from you.
Just extract.
Don’t think.
The few times when I felt I was challenged was when the numbers did not match with some of the other department’s numbers, that was when I had to go into the script and fix some code.
But even then, I just reported the corrected numbers in the end, no one really cared what I did.
You may argue, you were a rookie.
They wouldn’t have entrusted you with the heavier tasks since you were new there.
Well, I was fully responsible for the Personal Loans & Hire Purchase portfolios alone.
I also talked to my colleagues about it, and they mention they were doing similar things but in different shapes.
Basically, the things you would learn more are about banking knowledge instead of data, which isn’t a bad thing, it’s just not what I wanted.
The most interesting kind of task I came across which was presented by my superiors was a statistic on the probability of a person buying a certain product.
Basically, it went along the lines of — a customer with income higher than X dollars are X% more likely to buy this product.
I found that to be extremely meaningful at the time.
However, thinking back, I was not even close to be given the chance to perform tasks as such.
Additionally, it doesn’t seem that impactful compared to what I am contributing in my company right now.
In my opinion, I think it’s simply because of the nature of the business in banks.
The way their business model is designed limits the improvement/ innovation of their products.
Loans and deposits are always gonna be loans and deposits, there isn’t any mind blowing product to be implemented (or is there ?).
Since it is a stable business and majority of us will come across banks one way or another, they don’t really see the need of implementing high end AI/ machine-learning models/exploratory analysis to assist them in their business.
It’s just not worth their time and effort.
It may be different for some for you guys, but in Malaysia, I can speak for the majority of banks.
Compared to a tech company which is constantly thinking of ways to improve their product, be it recommendation algorithms, image and speech recognition, machine learning models to predict trends or even real time data products, I find it more exciting to be a part of that world.
Photo by Riccardo Annandale on UnsplashTech firms encourages innovationThe digital era.
Let’s for a moment think how would companies even get their data to begin with.
It’s through technology — website, apps, on-prem programmes.
Without them we would be storing all our data on stacks of papers in store rooms, flipping through them when we need something.
The thing about technology is — it is always evolving.
With the amount of new sites and apps being developed, people are lost in data.
Frankly, they do not know what to do with it.
For the few who do, they may have only scratch the surface.
This is one of the reasons why many stakeholders do not dare to invest into data science — they do not trust it.
The few largest tech companies like google, facebook and twitter are leading us into what data can do and how powerful it is.
This is encouraging some of the smaller tech companies to follow their footsteps, trying to implement the latest technologies in data.
Not all companies (especially non tech) have the guts (or the brains) to explore new technologies, they are just focusing on making profits with their traditional business model.
In the company I work in, we store our data in google cloud, bigquery.
This may be more important than you think it is.
If your company is spending money to invest in good technologies in their data, it means that you have the chance to explore/implement new things that are only limited to what you can learn.
Here’s an example, my company has recently migrated to bigquery from redshift and with that, they require data engineering to move all the data pipelines from where they have it previously into bigquery.
After many discussions, the company has decided to use Apache Airflow managed by Google Cloud Composer to create the pipelines.
Since this is a relatively new software by google, there isn’t much information on the internet about this product except that it is good at what it does.
Taking this as an opportunity, I jumped into Airflow, learning everything as fast as I possibly could and presented the pipeline to my superior.
One thing lead to another, the production code deployed onto airflow to grab data on a daily basis is written by your’s truly.
I am also the pioneer for hourly data extractions.
Before me, the company was only implementing daily extractions.
This is huge.
Not everyone get’s an opportunity like this.
It speaks volumes.
I contributed something huge in the company, and I also have a project to present during future endeavours ( which trumps me over my competition ).
The company also values me more as I am the go to person when there is a problem/improvement to be made to the pipeline ( see why I mentioned I was a big deal ? ).
All in all, there will be new things to learn every day in a tech company.
This is where, if you’re fast, get to show off your skills and maybe in return, earn that good appraisal that you’ve been longing for.
The flip side can be said for banks.
In the first ( and the largest bank in my country ) that I worked in, even the computers were ancient.
They were still using the computer block monitors as their displays while I am using a Macbook Pro at work now.
Their data was stored on prem and in SAS code, which makes moving to other technology 10 times harder.
You will also notice that the management have no interest in throwing themselves into deep/machine learning fields.
They have been achieving results without these technologies and will not change if they do not see the potential gain from it.
No one wants to make a change if the current system is already working.
One of the many reasons you learn slow in banks is, they limit your internet.
Nuff said.
You can’t log onto Youtube.
You can’t log onto Udemy.
You can’t log onto anything.
How am I suppose to debug my Python code if I can’t even go onto Stack Overflow ?.I can’t.
This is a huge barrier for the employees to pick up new skills, innovate, and create any change.
In my current company, we have a corporate account with Udemy which allows me to log onto Udemy and enroll in any course for free.
Do you want to learn Python from a well known paid course ?.You can here.
Another important thing to note is, if you have spent 3–4 years in banks, you would inevitably learn SAS as your main programming language for data.
From my knowledge, SAS is widely used in the banking and insurance industry.
The tech industry mainly uses Python and R for data because it is open source (free), easy to read and compatible with many technologies out there (AWS, Google etc).
They also do not use SAS because it is very expensive.
Hence, you are potentially locking yourself down into the SAS universe.
Which is ok if you prefer it.
But think about it.
If you are hiring for a tech company, would you prefer someone with 4 years with Python experiences and proven projects, or someone from the banking industry with SAS knowledge, keeping in mind that your company does not purchase a SAS license.
The answer is quite obvious.
You may argue that you can learn python on the side with some personal projects since you’re interested in it.
But that’s besides the point.
If you are that smart and have that much of time, you would be probably better off in tech to begin with.
However, if your dream is to work in banks to begin with, then you should probably disregard this article.
Tech firms have better working environmentPhoto by Nastuh Abootalebi on UnsplashThe culture of a workplace ,an organization’s values, norms and practices has a huge impact on our happiness and success.
— Adam GrantHave you ever dreamed of working in the legendary google work space.
Free food and drinks, amazing people, comfortable chairs, you get the point.
Well, we don’t have that here.
But we’re close.
For starters, the company I work in has an open space environment where I can just go up to anyone, anytime and ask anything I want.
This may not seem that important to you but trust me, it is.
Once you start to warm up, you get to socialize with people from different backgrounds and teams.
Work is also done faster.
For example, the marketing team often requests data from me for their operations.
I can, if I want to, just walk up to them and ask what is the data for.
In return, they will explain to me from start to finish, the background and context to what the data will be used for.
This increases the quality and efficiency of my work because I know exactly what they want.
In banks, we usually do this through phone calls.
You may argue that it does not matter much.
Here’s another example I have for you.
I recently initiated a personal project where I use real-time data to support a product on my company’s website.
Since it is a personal project, no one knows about it.
After I had gotten the ground work done, I have many questions to ask.
What is the opinion of the product manager on my project?.For example, how is real-time defined ?.Does he want it to be in seconds ?.Or in minutes/hours?.This is important as the more real-time it gets, the higher the load on the site, and the lesser the value of data will be shown (eg for the last 10 seconds, X amount of people have visited your site).
This requires experience.
How do I implement my real-time data onto the website and app ?.I’m a data analyst/engineer, not a website developer.
Is my project possible ?.Does it bring significant value to the company ?. More details