What does a data scientist do?

Most people are both impressed and confused when I tell them I'm a data scientist.

Impressed because it's considered such a fancy and prestigious title nowadays (though some will still call us statisticians who can code).

Confused because … what does Data Science mean, really? And what do we do?

Well, it depends.

On the domain, the company, and the team itself.

But in general, data science encompasses the following categories of work:

Databases and data engineering — Many data scientists work closely with databases, whether that's loading and querying large amounts of data, building data pipelines, or cleaning and preparing data for analysis. At my last company, I used SQL regularly to access our database in order to query data needed to build ML models. I also found myself creating and altering tables in order to store results from models and other analyses.
Data analytics and visualization — Data visualization involves not only analyzing the data but presenting it in a way that makes the information easy to interpret. Analytics can be a variety of things: Identifying and reporting key performance indicators (KPIs — for example, the percent increase in sales from one year to the next), trends (such as a correlation line chart) or other relevant conclusions about a dataset. Tableau is a popular tool used for data visualization and analytics as it easily allows you to create dashboards with lots of information. Great visualizations can also be built in Python or JavaScript.
Machine Learning and predictive modeling – This is what people typically imagine a data scientist to do – using statistical and mathematical models to predict some future outcome. And while it is personally my favorite part about the job, it's not the only thing we do and there's certainly a lot more that goes into ML behind the scenes. Lots of data retrieval, cleaning and preprocessing also needs to happen in order for this to be successful.

Data scientists may focus heavily on one, or all, of these areas. But regardless of what their specialty is, all data scientists are at least familiar with and capable of executing tasks in all 3 categories, and will likely do so many times throughout their career.

A Day in the Life of a Data Scientist

How can I get into data science?

The easiest and most efficient way is to get a college degree. Undergraduate or graduate.

I went this route and got an undergraduate degree. I think it's probably one of the few remaining college degrees that's actually worth it because since the salaries for data science positions are on the higher side, you'll be able to pay off your debt faster.

Obviously, this is not the only way.

Many data scientists are self taught. You can take online courses, earn certifications, and build up a repository of personal projects. This route takes a lot of hard work and dedication and discipline.

I liked college because it gave me structure.

There's also another route that's kind of in between, and it's one that I have seen many people do successfully.

That is, to get into data science through your current career.

At your current company, you most likely have a data science team somewhere.

Get in contact with them. Express your interest in the field. Ask them questions. Ask if someone can mentor you. Ask if you can collaborate on a project.

Then, ask your manager if the company will pay for you to take courses or certifications (Most will). You just may end up switching positions within your company and transitioning into the data science team.

And once you have this experience under your belt, you can transfer into a data science role at a new company, and continue to climb the ladder from there.

How much do data scientists make?

It depends on a few things.

Your geographical location. Country, state, and city.
Your education level. People with Masters degrees or PhDs tend to make more on average.
Your level of experience. Entry level data scientists will obviously make less than those who have been in the field for 10 years.

In the United States, the average data scientist salary is $122,861.

However, this is across all states, cities, and experience levels.

For more detailed information and advice on how to negotiate your first salary, as well as how to find out how much someone in your situation (geographical, educational, etc) should expect to make, check out the following article:

How to Negotiate Your Salary as a Data Scientist

How can I find a data science job in this difficult market?

I understand that it's rough out here at the moment.

There are 3 main things you should focus on:

Networking

This is one of the most effective things you can master in this day and age.

Referrals go a long way. When companies are looking to hire, staring at a resume just doesn't have the same effect as being told by a live person at the company: "Hey, I know someone who would be a good fit for this role".

When an employer stares at your resume and has a familiar face to relate it back to, you are much more likely to get an interview.

When you're searching for jobs, speak to your friends, family, and mutual friends. Search up their companies and see if they're hiring.

And of course, make the most of your LinkedIn. Reach out to old friends, classmates, coworkers.

How to Network as a Data Scientist

2. Strengthening your personal brand

This includes your LinkedIn profile and other relevant social media profiles. Have a clean, readable profile with a nice professional picture.

Take some time to work on the bio for your About section. Keep your work experience, certifications, and projects up to date, and add relevant details.

Make some posts (or even just repost things). This shows that you have a passion and interest in the field.

LinkedIn is how recruiters find you and if you can capture their attention you will get more interviews.

3. Building a strong portfolio

Whether it's making your own website, tidying up and populating your Github, or even starting a Medium blog, a portfolio is hard evidence that you can do the things you say you can on your resume.

This is especially important for people who have no data science work experience under their belt.

Doing personal data science projects, Kaggle competitions, or freelance work that you can publish to Github or some other website will really help employers and hiring managers to see tangible results from you and get a good idea of your skills.

What advice do you have for beginners?

Master the fundamentals.

Statistics, linear regressions, classification, data cleaning, preprocessing, and feature engineering.

All the fancy stuff will come. LLMs, neural networks, deep learning, sentiment analysis… it will all come much more naturally to you when you understand the building blocks of ML and data science.

Be patient, take it 1 day at a time, and embrace repetition. Embrace mistakes and bugs because they will happen, but eventually you'll be able to spot them and resolve them much quicker, and you can use your energy for grasping more complicated concepts.

The more you do something, the more you understand the process inside and out, and the more confidence you build in yourself and your abilities.

So keep going.