Data science is a compelling profession for logical thinkers who are great with numbers and Excel. But unless you’re lucky enough to know a real, live data scientist, it’s hard to imagine what they actually do Monday through Friday.

We’re shedding some light on this complex field so you can decide whether it’s the right path for you. Take a look at a typical data scientist job description and work environment, as well as the skills they use most.

We tied it all together by getting two insider perspectives from one of our Technical Experts, and a Thinkful grad who’s now working as a full-time data scientist. Find out what they do on the job, and why they love their careers in data.

What Does A Data Scientist Do?

A data scientist’s role will vary based on their industry and the goals of their organization. If you choose to pursue this wide-ranging career, you could be responsible for anything from improving an app by analyzing in-depth customer research, to predicting the outcomes of a new drug through machine learning.

But while the end goal varies, the basic steps to get there are the same. Here are the tasks that just about any data scientist will perform:

Matt Francsis, a Thinkful graduate who is now working as a Data Scientist for an analytics firm, discovers insights for clients in a variety of industries. Sometimes that means evaluating product performance for a pharmaceutical company; other times he’s interpreting sensory data for a company that makes wearable fitness trackers.

“A lot of my work is pretty variable: looking at experimental design, data analysis, data visualization and also feature engineering with sensory data and building analytical models to achieve some objective.” - Matt Francsis, Thinkful Grad & Data Scientist

Work Environment

Data science involves a lot of heavy computing (no surprise there). Whether your work is performed in an office or telecommuting, you’ll spend several hours a day in front of a screen. But the findings you reveal through your analyses and algorithms will have a tangible impact for your company, your customers, and possibly the world.

Data scientists bring a lot of value to their organizations, and they’re rewarded for their expertise in the form of high pay and excellent benefits. According to indeed.com, many full-time data scientists enjoy unlimited paid time off, food provided on-site and stock options. That’s in addition to the full suite of health coverage and 401(k) benefits.

If you commit to a career in data science, you’ll likely find yourself in high-level roles. Your insights will drive major company decisions, and that means a certain amount of stress and accountability. But you can also expect a lot of personal fulfillment and six-figure salaries.

Key Skills of A Data Scientist

Data science is a highly specialized profession that requires a variety of technical abilities. But it also takes interpersonal skills to excel: after all, you need to translate your equations into clear language in order to persuade the leadership team.  

If you have the following foundational skills, you’ll make an excellent data science candidate.

Hard skills: Prepare yourself for some pretty intense math, like advanced statistics, linear algebra and calculus. Every data scientist also relies on coding knowledge to interpret large datasets, so you’ll need to learn SQL and Python. And in order to enter this field, you need to be an expert at data organization and cleaning. If you’re brand new to the data scene, start by studying data analysis first.

Soft skills: You’ll need to communicate your findings visually and verbally in a way that makes sense to stakeholders. As a data scientist, you’ll have to perfect the art of taking complex information and pulling out the insights that matter most to business leaders. In addition to clear communication, a keen eye for detail combined with critical thinking skills will help you solve business challenges with equations and algorithms.

Most importantly, this field is ideal for someone who loves learning. Data science is constantly evolving, so you’ll need to adapt to new tools and technologies over the course of your career. It’s an ongoing challenge that many will find exciting and invigorating.

A Day in The Life of A Data Scientist

If you’re going to invest time and money into learning data science, you need to make sure you’ll actually enjoy the job. The best way to do that is to get honest answers from someone who’s already living and breathing data science.

We spoke with Alan Teran, a Technical Expert for Thinkful. He’s also a full-time Data Scientist for Boulder AI, a company that builds artificial intelligence tools that extract information from visuals. Here’s a breakdown of his work day, and the tools and skills that are crucial for his job.  

What does your typical day look like?

I typically work through all levels of a project from data collection, cleaning and preprocessing, to modeling, testing, validation and deployment. So depending on which stage we’re on, I’ll spend my day verifying or creating labels for images, building a model to extract whatever information our client asks for, and then validating our application in the real world.

Describe a recent project or accomplishment you’re proud of.

I worked on a project designed to improve the safety of pedestrians while they cross the street. We use a camera to detect the presence of a person in the crosswalk and send a signal to the controller if the person will likely not have enough time to cross before the light changes. I think this type of technology is really effective, helpful, and will eventually be brought to every city in the U.S.

"It’s really exciting to be at the head of the curve, creating solutions that can influence a lot of people in a positive way." - Alan Teran, Data Science Technical Expert

What hard and soft skills do you rely on the most?

Hard skills: Being able to write efficient, well written code. So much time is saved by not repeating previous work! Also the ability to learn new libraries and tools on the fly. We deploy our applications in C++, but I do most of our modeling in Python. So in order to make sure the code I write works in production, I’ve had to learn some C++.

Soft skills: The ability to plan my time and resources appropriately.  When our deadline comes, our products have to actually work. Really well. This takes a good amount of planning to know what to focus my time on, what to delegate to others, what an MVP looks like, and what needs to be pushed into the next release.

Another soft skill is the ability to really understand customer desires. Getting caught up in a purely technical approach might distract you from choosing a course of action that will actually satisfy the customer.

What tools do you use the most?

Libraries: Tensorflow, Pytorch, Caffe, OpenCV, Numpy

Languages: Python, C++, Bash

IDE: Visual Studio Code

Why do you love what you do?

It is about constant problem solving: building, developing, and providing insights in ways that were previously unavailable and sometimes impossible. It feels really great to have the chance to take part in building something that works well and hasn’t been done before.

The path to get there can be frustrating and there can be long stretches where seemingly no progress is made. But once you break through and look back, you see how much you've learned and what you’ve built. And it feels absolutely amazing.


How to Become A Data Scientist

This exciting field obviously requires a high level of technical knowledge. In order to land a high-paying data scientist position, you’ll need to know Tableau, code in Python, and work efficiently in a range of other specialized tools. And in order to progress long-term, you’ll need a deep understanding of machine learning and the underlying concepts, which range from calculus to the ethics of AI.

In our data science course, you’ll learn all of the above with the support of experienced instructors, technical experts and a personal mentor who’s already solving real-world problems as a data scientist.

“The value I think Thinkful provides that separates itself from doing study-alone, is really the number of touch points that you have as an individual student. You’ve got your mentor meetings, you’ve got your program manager check-ins, you’ve also got a really active slack channel that is staffed with technical coaches that you can ask quick one-off questions if your mentor isn’t available. And additionally the career services staff were also super helpful in rebranding myself as a data scientist and making sure that my data scientist job application profile matched what employers were looking for.” - Matt Francsis, Thinkful Grad & Data Scientist

Explore Data Science Immersion if you’re ready to commit to learning full-time, or Data Science Flex if you need to gain your new skills while holding another job. Either way, you’ll benefit from a thoroughly researched curriculum that’s designed to put you on a long-term, high growth career path.

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