I’m thrilled to sit down with Dominic Jainy, an IT professional with deep expertise in artificial intelligence, machine learning, and blockchain. With a keen interest in how emerging technologies shape industries, Dominic brings a unique perspective to the ongoing debate of cybersecurity versus data science—two of the hottest career paths in tech today. In this interview, we dive into the nuances of salary trends, skill requirements, and long-term growth in these fields, exploring what drives earning potential and how professionals can carve out rewarding careers by 2025 and beyond. Let’s get started!
What initially drew you to the tech industry, and how did you decide to focus on areas like AI and machine learning, which intersect with both cybersecurity and data science?
I’ve always been fascinated by how technology can solve complex problems, and early on, I saw AI and machine learning as game-changers across multiple domains. My interest in cybersecurity and data science stemmed from their real-world impact—protecting systems from threats and extracting actionable insights from massive datasets. I didn’t choose one over the other exclusively; instead, I gravitated toward the intersection of these fields, where AI can enhance security measures or drive data-driven decision-making. It’s the challenge of staying ahead of evolving tech landscapes that keeps me hooked.
How would you describe the core responsibilities in cybersecurity compared to data science, based on your understanding of these fields?
Cybersecurity is all about defense—protecting systems, networks, and data from attacks. It involves constant vigilance, identifying vulnerabilities, and responding to threats in real time. Data science, on the other hand, focuses on offense in a way—using data to predict trends, build models, and inform business strategies. While cybersecurity professionals are often behind the scenes ensuring safety, data scientists are more visible in driving innovation through insights. Both require analytical thinking, but the focus and outcomes are quite different.
Looking at starting salaries, data science seems to have a slight edge over cybersecurity at the entry level. What do you think contributes to this difference?
I think it comes down to immediate business impact. Entry-level data scientists often work on projects that directly influence revenue or customer engagement—think predictive analytics for sales or user behavior modeling. Companies see quick returns on those skills, so they’re willing to pay a bit more upfront. Cybersecurity at the entry level, while critical, is often seen as a cost center rather than a revenue driver, which might explain the slightly lower starting pay. It’s more about prevention than profit at that stage.
What skills do you believe are essential for someone just starting out in data science to secure a solid entry-level role?
For data science, a strong foundation in statistics and programming is key—think Python, R, or SQL. You also need to be comfortable with data visualization tools to communicate findings effectively. Beyond technical skills, curiosity and problem-solving are huge. Companies want someone who can ask the right questions of the data, not just crunch numbers. Familiarity with machine learning basics can set you apart, even at the entry level, since it shows you’re ready to grow into more complex roles.
How about for cybersecurity—what skills should beginners prioritize to break into the field?
In cybersecurity, understanding networking fundamentals and operating systems is a must. You’ve got to know how systems connect and where vulnerabilities lie. Skills in tools like Wireshark or Kali Linux can give you a leg up, as well as certifications like CompTIA Security+ to prove your baseline knowledge. Problem-solving under pressure is critical too—threats don’t wait for you to figure things out. And honestly, a mindset of constant learning is essential because the threat landscape changes daily.
At the mid-career stage, data scientists often out-earn cybersecurity professionals. What factors do you think create this pay gap during those years?
Mid-level data scientists often pull ahead because their work directly ties to business growth. They’re building models that optimize marketing campaigns or reduce operational costs, and companies can quantify that value easily. Cybersecurity pros at this stage are vital, but their contributions are often less visible—until a breach happens, of course. Plus, industries like finance and e-commerce, which rely heavily on data-driven decisions, tend to invest more in data science talent at that mid-level, widening the gap.
For cybersecurity professionals looking to boost their earning potential mid-career, what roles or skills would you recommend focusing on?
At the mid-career point, specializing in high-demand areas like cloud security or incident response can really pay off. With so many companies moving to the cloud, expertise in securing those environments is gold. Roles like security analyst or penetration tester also open doors to higher pay. Certifications like CISSP or CEH can validate your skills and push you toward six-figure territory. It’s also about showing you can handle bigger responsibilities, like leading a security audit or designing a defense strategy.
Shifting to senior roles, cybersecurity leadership positions like Chief Information Security Officer can command impressive salaries. What does it take to climb to that level?
Reaching a CISO level is about more than technical know-how—it’s about strategic vision and leadership. You need to understand the business side of security, aligning it with company goals and communicating risks to executives who may not be tech-savvy. Years of experience in managing teams and handling high-stakes incidents are crucial. Building a track record of preventing or mitigating major breaches helps too. It’s a role that demands trust, so soft skills like negotiation and stakeholder management become just as important as your tech expertise.
How do you see the global demand and salary trends for both fields evolving by 2025, especially with the influence of technologies like AI and blockchain?
By 2025, I expect both fields to see skyrocketing demand, especially as AI and blockchain reshape industries. In data science, AI-driven analytics will push salaries higher as companies race to stay competitive with personalized, predictive solutions. Cybersecurity will also boom—AI-powered threats mean more sophisticated attacks, and blockchain’s rise will demand new security protocols. Globally, I think we’ll see salaries in both fields continue to climb, especially in tech hubs, with cybersecurity potentially catching up to data science at senior levels as companies prioritize protection over innovation in uncertain times.
What’s your forecast for the future of cybersecurity and data science careers over the next decade?
Over the next decade, I see both fields becoming even more intertwined with emerging tech. Data science will likely evolve into more specialized niches—think AI ethics or quantum data analysis—while maintaining strong earning potential. Cybersecurity will shift toward proactive, predictive defense using AI, and roles like CISO will become as critical as any C-suite position. I believe both will offer incredible opportunities, but the balance of power might tilt toward cybersecurity as digital threats grow in scale and complexity. The key for professionals will be adaptability—staying ahead of tech curves will define success in either path.