Behind The Scenes: Explaining My Work As A Data Scientist

Author:Murphy | View: 28265 | Time: 2025-03-22 21:36:44

I have ~three years of experience working as a full-time data scientist, and in this article, I want to explain what exactly being a data scientist really entails.

The aim is to help anyone looking to enter the field to gain a truthful view of what a data scientist is, how we work, what work we do, and what a typical day in life is. I hope it can help you pinpoint if a career in Data Science is indeed for you.

Let's get into it!

What Is A Data Scientist?

Nowadays, data scientist can mean a lot of things, but I think of a data scientist as someone who uses programming, maths, statistics, and data to gain insights and produce predictive models to help a business.

You may have heard that a data scientist is a blend of software engineer, statistician, analyst, and mathematician. This is generally true, but data scientists often lean more toward one area than others, depending on their background, current role, and company requirements.

The main unique skill of a data scientist is modelling. We normally use Machine Learning and statistics to create models that can drive decision-making processes and produce predictions that are better and more efficient than if humans did them. That's where our main value lies as this process is greatly cost effective for a business.

For example, common problems data scientists work on are:

Determining whether a particular credit card transaction was fraudulent.
Recommending relevant products on e-commerce sites.
Predicting how the value of an insurance policy to offer a customer.
Forecasting stock for a supply chain

These four points truly show what a vast field data science is. It encompasses many areas, such as deep learning, forecasting, reinforcement learning, recommendation systems, optimisation, and many more.

What you find, therefore, is that as data scientists progress in their careers, they often specialise in a specific area, honing their skills and expertise within a particular industry. This helps them become more valuable later on and carve out their own niche in the market.

Check out my previous post to learn more about data science and other associated data roles like data analyst and data engineers.

Should You Become A Data Scientist, Data Analyst Or Data Engineer?

What Do I Do?

So, I work as a data scientist within a cross-functional team where my squad specialises in forecasting and optimisation based problems.

Much of my work revolves around improving our forecasting and optimisation models and providing insight and support to our key stakeholders to explain the decision-making.

The general workflow for most of my projects is as follows:

Idea – Someone may have an idea or hypothesis about how to improve one of our models.
Data – We check if the data to prove or disprove this hypothesis is readily available so we can start the research.
Research – If the data is available, we start building or testing this new hypothesis in the model.
Analysis – The results of the research stage are analysed to determine if we have improved the model.
Ship – The improvement is "productionised" in the codebase.

Along this process, there is a lot of interaction with other functions and roles.

The idea phase is a collaborative discussion with a product manager, who can provide business insight and any critical impacts we may have missed in the initial scoping.
Data, Build, and Analysis can be done in collaboration with data analysts and engineers to ensure the quality of our ETL pipelines and that we are using the right data sources.
The ship phase is a joint effort with our dedicated software engineers, ensuring our deployment is robust and up to standard with best coding practises.

From experience, I know that this type of workflow is prevalent among data scientists in numerous companies, although I am sure there are slight variations depending on where you are.

Another important thing to mention is that due to this collaborative approach, you often learn many useful tools and skills from the function. For example, you can write production code from the software engineers and build ETL pipelines from the data engineers.

What Is The Structure Of Data Science Teams?

Data scientists work in many different ways across an organisation, but there are mainly two options and the rest a mix between them.

Embedded – In this case, data scientists are embedded in cross-functional teams with analysts, product managers, and software engineers, where the team solves problems in one domain within the company. This is how I work, and I really like it because you get to pick up lots of valuable skills and abilities from other team members who are specialists in their own right.
Consultancy – This is the flip side, where data scientists are kind of an "in-house consultancy" and are their own team. In this scenario, the data scientists work on problems based on their perceived value to the business. You are technically less specialised in this option as you may need to change the type of problems you work on.

Both ways of working have pros and cons, and in reality, I wouldn't say one is better than the other and it's really personal preference. You still do exciting work, nonetheless!

What Is A Typical Day In A Life?

People online often glamourise working in tech, like it's all coffee breaks, chats, and coding for an hour a day, and you make well over six figures.

This is definitely not the case and I wish it was true, but it's still a fun and enjoyable workday compared to many other professions.

My general experience has been:

9:00 am – 9:30 am – Start at 9 am with a morning stand-up to catch up with the team regarding the previous day's work and what you are doing today. This daily ritual is not just a routine, but a powerful tool for keeping us connected and aligned in our goals.9:30 am – 10:30 am. After the standup, there may be another meeting for an hour, 9:30–10:30 or so, with stakeholders, engineers, or other data scientists to review some work.
10:30 am – 12:30 pm. Then, it's a work/code block for two hours where I focus on my projects. Depending on my work, I may pair with another data scientist or software engineer.
12:30 pm – 13:30 pm. Lunch.
13:30 pm – 16:00 pm. Another extended work focus block to get my coding and any other work done.
16:00 pm – 17:00 pm. Afternoon sync with stakeholders, engineers, or other data scientists to review some work. Sometimes there is a wider company meeting to discuss latest updates or other ad-hoc meetings that pop up throughout the day.
17:00 pm – 17:30 pm. Reply to emails, slack messages and wrap up for the day.

Every day is different, but this is what you can expect.

An important thing to note is that I don't always code in my work blocks. I may have a presentation to prepare for stakeholders, some ad-hoc analysis for our product manager or writing up some of my latest research. I may not even code for the whole day!

On average, I spend 3–4 hours hard coding, and the rest is meetings or some ad-hoc work. Of course, this varies between companies and also at different times of year.

NOTE: This is the day for a junior or mid-level data scientist (as that's my experience!). Senior data scientists and above will typically have more meetings as they are more responsible for guiding the direction and vision of the work than being always hands-on.

Why Am I Data Scientist?

The reason I am a data scientist can be boiled down to four main reasons:

Interesting. As a data scientist, I get to be right at the forefront of the latest tech trends like AI, LLMs, and machine learning. There is always something new and exciting to learn, which I love! So, if you want to constantly learn new skills and apply them, then data science may be a career you would be interested in.
Work-Life Balance. Tech jobs generally provide better work-life balance than other professions like banking or law. Most data science jobs are 9–6, and you can often spend a few days working from home. This flexibility allows me to pursue other passions, projects, and hobbies outside of work, such as this blog! Overall, its often a pretty low stress job, which is great.
Compensation. It's no secret that tech jobs provide some of the highest salaries. Particularly in America, it's not strange to find data scientists earning about $200k in many cities. In Europe, it's quite a bit less, but still higher than many other professions in a relative sense.
Range of Industries. As a data scientist, you can work in loads of different industries during your career. So far, I have worked in insurance and e-commerce, and I have only been working for 3 years! However, to become a real specialist, you need to find and stick to one industry you love.

Data scientists have it pretty good in my opinion!

Should You Be A Data Scientist?

Of course!

Anyone can be a data scientist and learn the maths, coding, and skills necessary to become one. However, that doesn't mean it's easy or as straightforward as you think. In fact, getting into data science can be a complex and lengthy process, but it is really worth it if that's the field you want to work in.

It's crucial to take the time for self-reflection and consider if data science aligns with your interests and aspirations. I've shared more insights on this topic in my other posts, which I encourage you to explore if you're contemplating a career in data science and you can then decide if it's really for you!

Why Data Science May Not Be For You

Data Science Advice I Wish I Knew Sooner

Navigating the Realities of Being A Data Scientist

I hope you enjoyed this article. If you plan on becoming a data scientist, I would love to hear your thoughts and hopefully this article can serve as a reference point!

Another Thing!

I have a free newsletter, Dishing the Data, where I share weekly tips for becoming a better Data Scientist, my general experience in the field, and some thoughts I have had over the past week.