How To Get A Job In Cybersecurity: Cybersecurity Job Requirements
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As the world becomes more dependent on technology for business operations and the exchange of information, companies simply can’t afford a data breach. Data breaches can cost millions of dollars and compromise sensitive information.
This has led to a growing demand for cybersecurity professionals across the globe. In fact, the U.S. Bureau of Labor Statistics projects jobs for information security analysts to grow by 33% from 2020-2030. This is four times faster than the projected 8% growth across all industries.
In this article, we’ll explore various paths toward getting a job in cybersecurity—even if you have no industry experience.
What Is Cybersecurity? What is cybersecurity? This field involves the protection of interconnected systems and networks from digital attacks, also called cyber threats. These threats range from hardware damage to information theft.
A cybersecurity specialist’s daily tasks may include the following.
- Implementing security audits across the company’s systems and networks
- Designing firewalls to prevent data breaches
- Training coworkers on IT security best practices
- Monitoring security systems to quickly detect vulnerabilities
These are some primary duties you can expect to take on, but your specific tasks may vary depending on your position and expertise.
Cybersecurity Job RequirementsCybersecurity is a high-tech field that requires speed, accuracy, and problem-solving abilities. It sets a higher bar for entry-level jobs than many other industries.
Below, we’ll explore some vital criteria that cybersecurity job candidates should meet.
Education RequirementsLike in any other industry, formal education is the easiest route to getting a good job.
The National Security Agency (NSA) recommends a bachelor’s degree in computer science or a related field such as math and engineering. Some positions even prefer a master’s in cybersecurity.
Some cybersecurity Job requirements favor practical skills over degrees. So if you’re unable to invest the time or money required for a traditional cybersecurity degree, your best bet is to attend an immersive cybersecurity Bootcamp.
A cybersecurity Bootcamp is an intensive training program that equips you with skills needed in the cybersecurity job market. Most full-time boot camps last four to 20 weeks. Part-time programs can run for up to a year.
According to a report by RTI International, a Bootcamp costs $11,900 on average. It’s much cheaper than a public college’s average $9,400 yearly tuition, which adds up to $37,600 for a typical four-year degree.
Popular cybersecurity boot camps include the following.
CertificationsCybersecurity Job Requirements don’t start and end with formal education. With a certification, you can establish professional credibility and boost your chances of landing the job.
Some popular certifications commonly required for entry-level cybersecurity jobs are:
- CompTIA Security+ is a globally recognized certification for entry-level cybersecurity professionals. It proves that you can identify, analyze and respond to security breaches in a system network. This certification is ideal for those who want to work as IT auditors, cybersecurity analysts, cloud engineers, or systems administrators.
- GIAC Security Essentials Certification applies to entry-level security managers, IT engineers, forensic analysts, and penetration testers. You don’t need years of experience to acquire this certification.
- Certified Ethical Hacker (CEH): EC-Council offers this certification to entry-level ethical hackers and system administrators. Candidates must meet the following qualifications:
- Be a CEH member.
- Have a minimum of three years of experience in InfoSec.
- Possess an equivalent industry certification.
- Cisco Certified CyberOps professional certification: This certification validates your ability to detect and respond to cyber threats. It has a three-year validity.
As you progress in your career, you can acquire advanced certifications such as Certified Information Systems Security Professional (CISSP), Certified Information Security Manager (CISM), and Certified Information Systems Auditor (CISA), each of which requires at least five years of full-time work experience.
Hard SkillsDue to the delicacy of digital assets, entry-level cybersecurity jobs requires more technical expertise than other fields. To get your foot in the door, you must be proficient in the following.
- Programming languages like Java, Golang, Python, and C++
- Project management
- Information systems
- Intrusion detection
- Risk assessment
- Accounting (for IT auditors)
Soft SkillsHard skills may land you the job, but your personality helps determine how quickly you climb the corporate ladder.
Every cybersecurity professional aiming to thrive in the workplace needs a combination of skills in communication, presentation, critical thinking, problem-solving, networking, teamwork, and time management.
Popular Cybersecurity Jobs Cybersecurity EngineerAverage Annual Salary: Around $99,000Education Needed: Bachelor’s degree in computer science or a related field career Overview: A cybersecurity engineer designs, implements, and monitors secure network solutions that defend an organization’s systems from hackers. If you’re wondering how to become a cybersecurity engineer, know that this role requires proficiency in Linux, vulnerability assessment, network security, and information systems.
Information Security AnalystAverage Annual Salary: Around $74,000Education Needed: Bachelor’s degree in computer science or associate degree in information technology career Overview: An information security analyst monitors organizational systems, detects security vulnerabilities, and proactively recommends solutions to their higher-ups.
Information Technology Support TechnicianAverage Annual Salary: Around $48,000Education Needed: High school diploma and certificate in IT support career Overview: An IT technician diagnoses and troubleshoots hardware/software problems for users and employees.
How to Get a Job in Cybersecurity Get an EducationMost cybersecurity Job Requirements pertain to your knowledge of the role. College, immersive boot camps, and self-education can equip you with the skills you need in network security, systems administration, and security auditing and response.
Gain Internship ExperienceInternships allow you to learn from seasoned cybersecurity professionals and gain hands-on experience. An internship can give you a competitive edge over applicants who have no experience at all.
Build a portfolio cybersecurity portfolio cements your credibility as an information security professional with skills and experience.
To build your portfolio, consider working on open-source cybersecurity projects, participating in hackathons, and sharing your knowledge on public platforms. You can host your projects in a private GitHub repository and grant access to hiring managers on demand.
Apply for PositionsGaining professional work experience is the best way to jumpstart a cybersecurity career. You can find entry-level cybersecurity positions on job boards, company websites, and social media platforms like LinkedIn.
U.S. citizens can also apply for cybersecurity jobs with the federal government via USAJobs.
Consider Obtaining CertificationWhile cybersecurity certifications are not required for most entry-level positions, these credentials can improve your marketability as a candidate. As you gain more full-time work experience, plan to earn relevant industry certifications as well.
IT career roadmap: Data scientist
Data science involves using scientific methods, algorithms, and systems to extract insights from structured and unstructured data. As a discipline, data science synthesizes mathematics, statistics, computer science, domain knowledge, and other inputs to analyze events and trends.
In a world gone digital, data scientists are among the most highly sought IT professionals. Fundamentally, a data scientist should be able to write clean code and use statistics to derive insights from data.
According to the career site Indeed.com, data scientists not only combine mathematics and computer science but must understand the industry they serve. Data scientists use unstructured data to produce reports and solutions related to their field.
According to Indeed, data scientists should be familiar with cloud computing, statistics, advanced mathematics, machine learning, data visualization tools, query languages, and database management. The ability to program with Python and R is generally expected.
The staffing firm Robert Half notes that landing jobs in data science, particularly at the entry-level, is not insurmountable. Despite recent cutbacks, recruiting for the technology sector remains active, as IT employers are hiring at or beyond pre-pandemic levels.
“As businesses accelerate their digital transformation, data scientists are needed across all major business sectors—from technology and manufacturing to financial services and healthcare—as well as organizations in academia, government, and the nonprofit sector,” says Robert Half. “That’s because organizations of all types need to turn numbers into recommended strategies and actions.”
To find out what’s involved in becoming a data scientist, we spoke with Daryl Kang, the data scientist at mobility-as-a-service provider Uber Technologies.
LEGDaryl Kang is a data scientist for Uber Technologies.
Kang earned a Bachelor of Arts degree from the University of California, Los Angeles, where he majored in business economics with a minor in accounting. “I was a first-generation college student,” he says. “I graduated summa cum laude in 2.5 years, which allowed me the financial wherewithal to pursue graduate school.”
Kang went on to pursue a Master of Science degree in data science at Columbia University. Qualifying for the data science program required a foundation in math, probability, statistics, and computer science.
“I was originally motivated to pursue a career in banking and finance,” Kang says. “Having graduated with a degree in economics, I had assumed this to be the most natural career path.”
However, during a gap year after finishing college, Kang had the opportunity to work on personal projects that aligned with his passions. “I was motivated to major in economics after being inspired by the book, Freakonomics,” he says. “It showed me the power of data in answering questions that were universally applicable to any field.”
Around this time, Kang also discovered a passion for programming, after “running into the ceiling of what was possible with Excel,” he says. He devoted several months to learn how to program through free online courses.
It’s important to know the difference between a positive and a negative challenge. Quitting the wrong pursuits enables us to focus on the things that matter.“This set me on a clear path to eventually discovering the field of data science, and with it the clarity of recognizing it as a continuation of my passion for economics,” Kang says. “At this point, I was determined to pursue my graduate studies in data science to make the career switch.”
Foundations: Discipline, passion, and empathy growing up in Malaysia, Kang says he experienced a strict public education system, “where discipline was a key value that was instilled in me. This definitely set the stage for building a strong work ethic that helped in my data science career, since the role can be demanding.”
In addition, Kang’s experience in a liberal arts program at UCLA helped foster a sense of appreciation for other fields of study, and a general desire for learning. “This gave me the discipline, but more importantly the passion, to pursue continuous learning that is essential to keeping up with the field of data science,” he says.
Kang also notes that starting from a non-technical background helps him empathize with non-technical stakeholders, which he uses to communicate effectively in his role.
Employment history king’s first exposure to working in data science came in an internship with the entertainment company Viacom (now Paramount). He spent seven months working as a data scientist intern. “This was my first real experience with data science in the industry,” he says. “I worked on predicting box office revenues.”
The experience was instrumental in helping Kang bridge the gap between academia and industry. He was able to identify the gaps in his skill sets that he would need to close in order to succeed in applied data science, he says.
In 2018, Kang joined the media company, Forbes, as a data scientist, focusing mainly on building recommendation systems. One example was a system that recommends trending news articles to writers in the newsroom.
“There was a heavy emphasis on back-end engineering, and it gave me an opportunity to better improve my software engineering skills,” Kang says. “It was also an opportunity to experience the end-to-end lifecycle of delivering a data product, from setting up the back-end infrastructure to parsing insights from the data to surfacing those insights to the end user.”
To be effective in his role at Forbes, Kang needed to have a solid grounding in Python and software architecture.
After about three years at the company, Kang joined Uber as a data scientist in a role heavily focused on product analytics. “I worked specifically on merchant growth and acquisition. This meant that the deliverables were focused more on informing business decisions and making product recommendations.” Kang notes that data engineering was also a significant part of the role. “Data from a multitude of sources had to be consolidated to properly communicate the state of the business.”
At Uber, Kang says he has had to be well-versed in experiment design, “which forms a core part of Uber’s principles in making data-driven decisions.”
A data scientist’s typical workweek“Meetings, unsurprisingly, are a key part of the week,” Kang says. “These are opportunities to deliver reports, presentations, and build empathy for stakeholders.” Oftentimes these stakeholders are product managers, though it is not uncommon to collaborate with other job functions such as user experience researchers, product designers, or engineers.
“Depending on the projects at hand, the rest of the time could be spent doing analytics—for example running descriptive analytics to prepare a monthly performance report or diagnostic analysis to investigate a change in a metric—crafting presentations, or more specifically defining the narrative and arriving at recommendations,” Kang says.
Memorable career moment“One of my favorite memories from my time at Forbes was from mentoring a team of graduate students through their capstone project as part of an industry outreach program,” Kang says. “It was refreshing to play the role of mentor for the first time, and it was as much a learning experience for me as it was for the students. That the team also won first place in the end-of-semester capstone showcase competition was just the icing on the cake.”
Career advice“Fortune favors the bold,” Kang says. “Many things seem insurmountable at the onset but will ease with time and repetition. Also, it’s important to know the difference between a positive and a negative challenge. Quitting the wrong pursuits enables us to focus on the things that matter.”
Practically speaking, Kang recommends anyone interested in data science should start by learning Python and statistics. “If you’re undeterred and curious enough, you will naturally fall into the fields of data science and machine learning next.”
Your Recruitment Data Is A Goldmine—Are You Utilizing It?
Chief Strategy Officer, SEI Boston.Getty
If your recruiting strategy consists of listing a position on a job board and sifting through the stack of Robo-applies to find the one diamond in the rough, it’s time to change. The talent climate isn’t what it used to be and will continue to evolve as prospective hires flood the market in search of better pay, flexible schedules, and companies that align more closely with their values. Applicants have more bargaining power than ever before and are turning the tables on hiring teams by being the first to ghost a company if the recruitment experience doesn’t deliver.
With companies vying for the attention of millions of job seekers, leaders are feeling the pressure to find and retain top talent. Because hiring is such a volume-based competition, many teams are simply throwing everything at the wall and hoping something sticks. The truth is, something probably will stick—but that something might turn out to be someone who wasn’t suitable for the position and may even quit within a few months. Now your company is back to square one.
As the job market morphs, so should our recruitment models. In the age of big data, it’s time to take a data-driven approach to hire and pair our intuition with tangible metrics.
To Be People-First, You Need To Be Data-Driven
Data can say a lot about your recruitment strategy and, more importantly, it can reveal game-changing insights into your company culture. Given that 86% of job seekers are looking for companies actively investing in DE&I initiatives, it is crucial that we consider what our data tells us about how we’re recruiting, who we’re recruiting, and where and why. Employees want a company that’s people-first, and while utilizing a data-driven hiring strategy may sound counterintuitive to reaching that coveted distinction, it’s quite the opposite.
Recruiting Generates A Lot Of Data
For many companies with dedicated talent acquisition (TA) teams, the recruiting process generates huge amounts of data daily—data that can provide invaluable insights, if leveraged correctly, that a lot of aggregators would pay good money to access. An applicant tracking system (ATS) can compile compelling submission data, including age, where people live and work, their career trajectory, the length of time they held previous jobs, certifications, software proficiencies, and more.
By categorizing and analyzing that data, TA teams can begin to identify areas for improvement. If you’re getting a lot of one type of applicant, how do you need to change your strategy to be more inclusive to a broader group of candidates? If most of your submissions are from entry-level professionals for a senior-level position, how can you change the phrasing of your listing to clarify the nature of the role?
With well-organized data, you can streamline your screening, sourcing, and recruitment processes and better position your brand’s people-first practices to attract the right talent.
Overhauling Your Candidate Sourcing Strategy Using Data
Hiring is costly—not only from a monetary standpoint but also because it affects productivity. Research from SHRM shows that the average cost per hire can range up to $4,700 and, shockingly, that number could be even more than three to four times the position’s salary thanks to indirect costs. As outlined by SHRM, if the hiring process for a position with a salary of $60,000 costs you as much as $180,000 to fill, you want to ensure you’re getting the most for your money.
Using data to overhaul your talent sourcing strategy doesn’t just save your company time, effort, and money. It could significantly impact how talent views your brand, too. According to findings from Greenhouse, 60% of job seekers are bothered by time-consuming recruiting processes and want companies to refine the candidate experience. More than 60% said that if a company provides interview feedback, they’d be more likely to apply to other open positions within that same company. In a competitive job market, that means better brand perception and a wider pool of passive candidates for your team to source from.
Other ways you can use data to optimize how you hire include:
• Maximizing the effectiveness of inbound recruiting, which is ordinarily the least productive
• Establishing a better rapport with recruits from the start
• Benchmarking metrics you can use to track the success of campaigns
• Giving senior-level members and stakeholders insights into what the current talent climate looks like
Overcoming Recruitment Challenges
As you begin testing and understanding how data can help elevate your hiring process, you may encounter a few challenges. Knowing these from the outset can help you develop ways to avoid them and make the most out of your next hire.
Getting Bogged Down By Data
TA teams can quickly become overwhelmed by the massive amounts of data generated by applicants. Simplify the process by designating a few KPIs that are important to you. Whether that’s time-to-hire, acceptance rates, or even the median age of applicants, picking a few generalized metrics to track can help you sift through the granular details and develop actionable next steps.
Forgetting About Internal Mobility
Focusing on data from external candidates can sometimes lead you to forget about nurturing talent you already have in-house. Continue using data to your advantage here and consider using internal surveys to gauge how many employees would be interested in a promotion or transfer. Then, set up a talent marketplace to start actively recruiting from within.
Go With Your Gut (And The Data)
Even the most comprehensive data can’t match your human intuition for sensing whether or not a candidate will align with your company’s values and become an important member of the team. If you want to improve your recruiting process and build a solid foundation of talent to grow your company, compare those gut feelings to your data and see how they measure up. Ultimately, you’ll find that maybe you were doing the right thing all along, and now you have the data to prove and improve upon it.
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