How to Get a Job in Data Science

How to Get a Job in Data Science

Jobs centered around big data are in demand. The Bureau of Labor Statistics notes that data scientists are among the fastest growing occupations through 2030.1 Companies need employees who have the skills to work with and leverage data to better drive decision making, budgeting, resource allocation and, ultimately, profits. 

Data scientists and other data science roles drive this critical work for companies that realize the true impact of big data. In brief, data scientists prepare data for analysis and ultimately draw explanations and actionable insights from that data.2 

It’s not too late to make a career change to get into data science or accelerate your career if you work in an adjacent field, like information technology. We have gone into greater depth about how to get into data science here on the blog. In this post, we will learn more about how to get a job in data science.

Popular Jobs in Data Science

Assuming you have the education and any requisite experience to begin a job search in the data science sphere, the first step is to gain familiarity with roles that fall under the data science umbrella and what they require in terms of skills. The list below is not exhaustive, but meant to give you an idea of what jobs might look like in this space, and in turn, how to get a job in data science. 

Data Scientist

A data scientist is likely the first role that comes to mind when thinking about careers in data science. As a data scientist, you will collect, analyze and interpret large amounts of data in order to help drive solutions to overcome various business challenges. In order to break into a data scientist role, you will likely need skills in a variety of areas, including machine learning, databases, coding language and more. Problem solving skills are also key. 

The average salary for a data scientist is $117,212 per year and people in this position typically hold an undergraduate degree in statistics, math, computer science, or economics.3

Machine Learning Scientist

Machine learning scientists/engineers design automated software to create predictive models. They also serve as the intermediary between software engineering and data science teams, and they will often work with data scientists to feed organized data into the models their software has created. They teach machines to identify trends or patterns in programming data and may work with engineering leaders to transform research into AI capabilities.4

Professionals in this role redefine raw data into data science models that are ready to scale.

Machine learning specialists earn an average salary of $137,053 per year, and need to have a bachelor’s degree in computer science or a similar field.4 

Applications Architect

Applications architects, also known as software architects, are responsible for creating new software for companies, as well as managing large websites. They will often lead programming teams that work to create a positive and smooth experience for customers using any company software. To succeed in this role, you will need both coding and leadership skills. Specialized certifications are also important in order to stand out to employers. 

An applications architect earns an average annual salary of $129,000 and will typically hold an advanced computer science degree, or a degree in computer engineering.5

Data Architect 

Data architects, sometimes called database architects, manage databases and organize data in order to aid companies that want to grow in a current market or enter new markets. They help in this area by evaluating the employer’s needs and making database changes accordingly. You will need coding experience, problem solving skills and strong communication and collaboration skills to work in this role. 

Data architects normally hold a degree in computer science or information technology and earn an average annual salary of $118,868.6 

Prepare Your Resume and Portfolio

After you have a better understanding of the role that you are looking for and what is required based on the job description, you will want to make sure that your resume is up to snuff. We have covered data science skills you will want to have on your resume, and have gone over how to craft a standout data science resume

The former blog post goes in depth on nine skills you will want to demonstrate through your resume, including programming languages, data visualization and storytelling, deep learning and analysis. The later blog post on developing your resume will walk you through best practices, including how to choose a template, how to highlight your skills and data science proficiencies, data science keywords to include and more, ultimately giving you key tips on how to get a job in data science. As a general rule once you have crafted your resume, make sure that it is error-free. It is always a good idea to have someone else review your resume for you, as they have a fresh set of eyes and will catch errors you might have missed. 

In addition to your resume, maintaining a portfolio that showcases the best of your past work is key to showing potential employers what you can do. If you do not have past work experience to put into your portfolio, create projects using actual data that you can showcase. GitHub is a popular place to post these projects.

Prepare for the Interview

With a strong resume and portfolio in hand, you will move on to the next step in pursuit of figuring out how to get a job in data science—the interview. If you haven’t interviewed in a while, 

it is a good idea to brush up on interview best practices. Research all that you can about the company you will be interviewing with ahead of time, get a sense of the most common interview questions you may be asked and have anecdotes ready to share that demonstrate how you will be a good fit for the company and the position.7 

Once you have brushed up on the general interviewing, be sure to review common questions that will be asked in a data science interview and think about how you will answer these questions. In addition to personal questions that your anecdotes will address, be ready to answer questions about statistics, machine learning, modeling and programming.

Data Science Skills Are In-Demand

To move from Googling “How to get a job in data science,” to declaring, “I landed a job in data science,” preparation is key. It is also  important to keep your education and skills up to date. Consider an online data science course from EmergingEd to help you grow your skills set and stay relevant in the job market as you look to make your next career move.

Explore the EmergingEd data science courses.



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