Interview with Ndidi Nzeako Anyakora, The Lead Data Scientist at INTELLIGENT CORE

My path into data science and AI wasn’t linear, it evolved through hands-on experience across a range of industries including oil and gas, retail, energy, education, and consumer technology. Working in these different sectors exposed me to a common thread: the power of data to solve real problems, drive decisions, and create efficiency at scale. Whether I was building dashboards for warehouse inventory, designing KPIs for inspection workflows, or detecting fraud in high-volume claim systems, I kept finding myself drawn to the role of data in unlocking insight and impact.

This cross-industry journey sharpened my technical skills and gave me a broad perspective on how adaptable and powerful data science can be. It also fueled my transition into more advanced roles where I could build machine learning systems and lead data-driven projects from idea to deployment. Along the way, I pursued further education, earning degrees in engineering and advancing to PhD research focused on AI for next-generation industrial networks.

The challenges were real; switching industries, learning new domains quickly, and often working in male-dominated spaces where I had to prove my competence and earn my place. At times, imposter syndrome crept in, especially when stepping into environments that demanded both technical depth and business fluency. But my diverse experience became my strength. It taught me how to adapt, how to listen, and how to lead with both empathy and technical rigor.

Today, I work in AI not just because I enjoy building models but because I’ve seen, firsthand, the value of data across industries. I’m motivated by the opportunity to use that knowledge to make systems smarter, people’s work easier, and businesses more effective.

I’m excited about the future of AI, data science, and machine learning; it’s a dynamic time to be in the field. AI is becoming more integrated into everyday life, not just in tech but across healthcare, finance, education, and manufacturing. In the coming years, it’ll likely become as common and seamless as electricity quietly powering personalized assistants, smart cities, and decision-making tools in areas like climate science and medicine.

The pace of progress has been remarkable. When I started, machine learning felt niche. Now, open-source tools and cloud platforms have made advanced AI more accessible than ever. Deep learning, especially in vision and language, has pushed boundaries, and generative AI has gone from concept to everyday use. Meanwhile, data science has become a key business driver, with growing attention on ethics, privacy, and responsible use.

Looking ahead, I see AI becoming more efficient, ethical, and explainable. More technologies will bring real-time intelligence to small devices, and stronger governance will help build trust. I’m excited to be part of this evolution shaping AI that’s not only powerful but practical and people-centered.

AI is making a significant impact on the oil and gas industry, even though it’s a sector sometimes seen as traditional. In my experience, oil and gas companies are increasingly using AI-driven solutions to improve efficiency, safety, and decision-making. One big impact area is predictive maintenance: using machine learning to analyze sensor data from equipment (like drills, pumps, pipelines) to predict failures before they happen. This has a huge payoff in oil and gas because unplanned downtime or accidents can be extremely costly. By catching an anomaly in pressure or temperature trends early through AI models, companies can schedule maintenance proactively and avoid a potential breakdown or spill.

Another area where AI is powerful is in analyzing the massive geological and seismic datasets for exploration.

Traditionally, geologists would manually interpret seismic images to find potential oil reservoirs, now computer vision and deep learning models can sift through this data faster and sometimes identify patterns humans might miss. This means improved accuracy in locating new oil and gas reserves and optimizing where to drill. I’ve also seen AI being used in reservoir management for example, algorithms that optimize how to inject water or gas to get the most out of a reservoir over time, adjusting as conditions change.

Safety and compliance benefit from AI as well. In operations, computer vision systems can monitor sites in real time (for instance, checking if workers are wearing safety gear or if there’s unexpected personnel in a restricted area). Drones equipped with cameras and AI can inspect pipelines and offshore rigs, reducing the need to send people into hazardous locations. And on the environmental side, AI models help predict and control emissions or detect leaks quickly, which is crucial for responsible operations.

As for which technologies have the strongest impact, I would say a combination of IoT (Internet of Things) sensors with AI analytics is at the top. The oil and gas industry has so much sensor data: pressure, temperature, flow rates, vibration and making sense of it in real time through machine learning is a game-changer. This combo drives the predictive maintenance and real-time monitoring I mentioned. Additionally, big data platforms and cloud computing are vital technologies, because they enable companies to store and process the enormous volumes of data generated by operations and run heavy AI models at scale. Machine learning techniques like time-series analysis, anomaly detection, and reinforcement learning are particularly impactful for optimizing production and detecting out-of-norm scenarios.

Data science plays a crucial role in enabling smarter analysis and decision-making in oil and gas. This industry has always been data-intensive, whether it’s geological data, drilling logs, production figures, or market prices and making sense of all that information is exactly what data science is good at. In practice, data science helps convert raw data into actionable insights that engineers and executives can use to make informed decisions.

For example, consider the challenge of deciding when to service a piece of equipment on a rig. In the past, it might be scheduled based on fixed intervals or someone’s gut feeling. Now, with data science, we can analyze historical performance data and real-time sensor readings to predict the optimal time for maintenance. This kind of predictive insight means decisions are backed by evidence, if an algorithm shows that a compressor is likely to fail in two weeks, managers can decide to replace it during a planned downtime rather than risking a breakdown. It takes the guesswork out of operational decisions and often saves money and improves safety.

On a strategic level, data science aids in decisions like where to invest and how to optimize assets. In one of my roles at an energy company, I helped build models to forecast market trends and customer demand. Our analysis provided clarity on which energy projects had the highest potential return. In fact, those data-driven reports directly informed leadership’s decision to invest in a new set of renewable energy initiatives; something they felt confident doing because the data supported the long-term value. That’s a great example of analytics influencing big-picture strategy. Similarly, in oil and gas, data-driven forecasting can guide decisions on things like how much capacity to allocate to refining vs. exploration based on projected demand, or whether to expand to a new field.

Real-time dashboards and data visualization are also a big part of how data science supports decision-making. By presenting complex data in an understandable way, like a dashboard showing key performance indicators across dozens of wells or pipelines, data science enables managers to grasp the situation at a glance and decide where attention is needed. I’ve built dashboards that pull together data from disparate sources. Once the right people had that unified view, you could see them make faster, more confident decisions because everything they needed was at their fingertips.

In essence, data science turns the overwhelming flood of data in oil and gas into insights and narratives that humans can use. It helps answer critical questions: “Are we on track to meet our production targets? Which asset is underperforming and why? What happens if prices drop next quarter?” By using statistical analysis, machine learning, and good old domain knowledge, data science provides evidence-based answers. This leads to decisions that are not just based on intuition or experience, but on a combination of experience and empirical evidence. That mix greatly improves the quality and outcome of those decisions. As a result, companies can operate more efficiently, strategically, and safely, guided by a clearer understanding of their data.

Working at Apple and Intel has been an amazing learning experience, and each company’s culture left a strong impression on me in different ways. At Apple, even during my relatively short stint there, I could sense the culture of innovation and attention to detail that people often talk about. Apple folks really care about the end product and customer experience, and that trickles down to every team, including those of us working on internal data projects. The environment was fast-paced and very collaborative. Despite Apple’s size, teams felt nimble and focused. I remember how every meeting, no matter how technical, would tie back to how it impacts the user or the business. There’s a saying at Apple about the “culture of quality”, and I saw that firsthand, whether it was in coding practices or the way presentations were meticulously crafted.

One memorable experience at Apple was seeing the impact of our fraud detection project in action. I was part of a team developing an ML system to catch fraudulent AppleCare+ claims. When we first started flagging suspicious cases with our model, it was exciting but a bit theoretical. Then one day I heard that our system had identified a ring of fraudulent claims that the company was then able to investigate and stop. That was a “wow” moment, the kind where you realize your work directly prevented loss and helped customers. The team was thrilled, and it really cemented for me what a data science victory feels like in a large-scale product context. Culturally, Apple also emphasized secrecy and focus. I was working under NDAs and often couldn’t discuss details even with colleagues on other teams, which was new to me, but it created this tight-knit trust within our team. Also, Apple has a surprisingly supportive culture for growth, people were open to answering questions and sharing knowledge (as much as their

confidentiality allowed) because everyone wanted the project to succeed. I felt respected and challenged, which is a balance I value.

Intel’s culture, on the other hand, reflected its legacy as a cutting-edge engineering and technology company. At Intel, I found a strong emphasis on rigor, process, and technical excellence. The work I did there was in system software validation for semiconductor products, which is as hardcore engineering as it sounds! The culture encouraged asking tough questions and thorough testing, nothing goes out the door at Intel without exhaustive validation, and I was proud to be part of that. One thing I appreciated was Intel’s tradition of “constructive confrontation,” meaning that in meetings, even if you’re junior, you’re encouraged to question and debate to make the product better. I never felt afraid to speak up or share an idea; in fact, it was expected. There’s a sense of pride among employees about Intel’s role in inventing the future (they literally build the chips that power the world), and that pride creates a very motivated atmosphere.

A memorable experience from Intel was the first time I walked through one of the fabrication labs (the fabs). I was in a bunny suit (the full-body cleanroom outfit) touring this ultra-modern chip manufacturing line, and I was just in awe. Robots were moving silicon wafers around, and machines were etching circuits at nanometer scales, it felt like being inside the heart of innovation. That day, I fully grasped how much precision and teamwork is required to ship something as “simple” as a processor chip. On a smaller scale, I also remember working late one evening with a team of engineers to troubleshoot an elusive bug in our log data. It was the kind of scenario where a bunch of us were huddled around a screen, combing through data and testing fixes. When we finally solved it, the cheer and high-fives we shared were pure joy. It’s funny how such moments which most people would call work become memorable bonding experiences.

In summary, Apple’s culture taught me about innovating with the user in mind and maintaining a high bar for everything you do, and Intel’s culture taught me about engineering discipline and not being afraid to dive deep and challenge ideas. Both places had incredibly bright people and supportive environments in their own ways. I feel fortunate I got to contribute to teams at both companies, because it broadened my perspective on how top-tier tech companies operate. Those experiences and the people I met have definitely shaped how I approach my work now.

AI is a fast-moving field, and as of 2025 there are several exciting trends on the horizon (and already underway). Here are a few AI trends I find noteworthy:

• Generative AI everywhere: We’re seeing generative models (the kind that can create text, images, music, etc.) becoming mainstream tools. From AI systems that can write code or marketing copy, to image generators used in design, creative AI is increasingly part of daily workflows. I think in 2025 this trend is only growing for instance, more companies will deploy AI chatbots or assistants fine-tuned for their domain, and contentcreation will be greatly accelerated by AI co-pilots. It’s not just about flashy demos anymore; generative AI is being integrated into real products and services.
• AI at the edge: With the expansion of IoT and 5G/6G networks, a lot of AI is moving from the cloud to the “edge” (local devices and sensors). This means things like smart cameras, industrial machines, or even your smartphone are running AI models on-site for instant decisions. In 2025, I expect edge AI to be more prevalent. For example, factories will have AI directly on the assembly line monitoring quality in real time, and cars will process data on-board for autonomous driving features without always needing to ping the cloud. This trend is driven by privacy needs and latency requirements, and it’s enabled by more efficient AI models and specialized hardware.
• Responsible and explainable AI: As AI systems influence more aspects of life, there’s a strong trend toward making them more transparent and fair. I see 2025 as a year when AI ethics and regulation take center stage. There’s growing interest in AI that can explain its decisions (so users trust them) and in practices that mitigate bias in AI models. We’re already watching governments propose AI regulations and companies establishing AI ethics boards. This trend means data scientists like me are spending more time on model interpretability and ensuring our algorithms are accountable. It’s a positive development that will help AI gain trust and be used for good.
• AI for industry-specific solutions: Another trend is the rise of domain-specific AI. Rather than one-size-fits-all models, we see tailored AI solutions for specific sectors. In healthcare, for example, AI is being used for drug discovery and personalized medicine. In agriculture, AI helps with precision farming. In my own area (let’s say energy and communications), we see AI optimizing power grids or managing network traffic smartly. By 2025, these industry-focused AI applications are more mature and widespread. This also means that the demand for AI professionals with domain knowledge (not just modeling skills) is increasing, a blend of data science expertise and industry expertise is the sweet spot.
• Continuous learning and AutoML: Tools that make building AI models easier (often called AutoML) are trending, too. We have platforms now that can automatically try out multiple algorithms and find the best one, which lowers the barrier for companies to adopt AI. And for AI models in production, there’s an emphasis on continuous learning systems that can update themselves with new data on the fly. In 2025, I expect to see more “learning on the job” AI: for example, a customer service AI that keeps improving as it chats with customers, or a supply chain model that adapts instantly to new market conditions. This trend ties into MLOps (machine learning operations) becoming a bigger deal as well, making sure models remain accurate and effective over time via automation.

These are just a few trends, but overall the theme is that AI is becoming more integrated, specialized, and responsible. It’s an exciting time because the technology is not only advancing in capability, but also maturing in how we deploy and govern it.

When I’m not working with data or algorithms, I make it a point to disconnect and recharge. One of my favorite ways to relax is traveling. I absolutely love exploring new places whether it’s a weekend road trip or a journey to another city, experiencing different cultures and cuisines really refreshes my mind. Travel gives me a sense of adventure and reminds me there’s a big world outside of work. Even just planning a trip or daydreaming about a destination can lift my spirits during a busy week!

Another hobby that’s close to my heart is dancing. I grew up dancing and still enjoy it as an adult. I’ll put on some great music (Afrobeats and pop are my go-tos) and just let loose. It’s such a fun way to relieve stress and express myself. Sometimes it’s just me in my living room having a mini dance party after a long day of coding, and I end up with a huge smile on my face. It’s like a reset button for my brain.

Aside from those, I also find deep fulfillment in mentorship and community work. For example, I volunteer with a women’s empowerment initiative and love teaching STEM to kids. Watching young minds light up when they grasp a concept or build something new is incredibly rewarding and reminds me why access to education matters.

There are a few things that really drive me to keep pushing forward, both in my career and in life. First and foremost, I have a genuine passion for learning and problem-solving. I’m one of those people who gets excited by a new challenge. If there’s a tough problem to crack or some new technology to figure out, I’m motivated to dive in. That intrinsic curiosity keeps me going, because there’s always something new to discover in the world of AI and data science. Even when I encounter setbacks (which definitely happen!), I see them as learning opportunities. The idea that tomorrow I could know or create something that I didn’t today is very motivating for me.

Another big motivator is the impact of the work. I really believe in the positive difference technology can make. Knowing that a model I built can, say, streamline a process or detect fraud or improve network performance in other words, make someone’s job easier or a product safer gives me a strong sense of purpose. When I see the tangible results of a project (like a successful deployment or a business improvement), it fuels me to tackle the next project with equal enthusiasm. I remember mentoring students during my time as a tutor, and seeing them land jobs or build cool projects was incredibly motivating. It’s that impact on people, colleagues, customers, or mentees that keeps me energized to do more.

I’m also motivated by the idea of being a role model and helping others, especially other women or underrepresented folks in technology. I’ve been fortunate to reach certain milestones (like working at top tech companies, pursuing a PhD, etc.), and I feel a drive to pay it forward. I want to show others that “if I can do it, you can do it too.” This is why I’m involved in community initiatives and mentorship. Seeing someone I’ve advised succeed or overcome a hurdle makes me so happy, and it pushes me to keep going and growing myself, so I can continue to be of help.

Lastly, on a personal note, my family, and support system motivate me. I come from a family that really values education and hard work my parents instilled in me early on the importance of perseverance. When things get tough, I think about the sacrifices my family made and the faith they have in me, and it reminds me why I shouldn’t give up. Also, I simply have some big dreams for myself, and thinking about those goals (whether it’s writing a book someday, leading a major AI initiative, or contributing to development back home) lights a fire in me.

In essence, it’s a mix of love for what I do, a sense of purpose, community, and personal aspiration that motivates me. Each day, even if it’s challenging, I know I’m moving incrementally towards making a difference and achieving something meaningful, and that feeling is priceless.

I wouldn’t call myself an avid reader, but there are a few books that have truly stuck with me and shaped how I think about my work and growth:

“AI Superpowers” by Kai-Fu Lee offers an insightful look into the development of artificial intelligence across the U.S. and China, and what it means for the future of jobs and society. It was eye-opening to see how different regions innovate and how AI might reshape the global workforce. It’s informative but very approachable, with personal stories that make it easy to connect with.

“Lean In” by Sheryl Sandberg also made a strong impression on me. As a woman in tech, her honest take on leadership, confidence, and navigating challenges in male-dominated spaces really resonated. It made me reflect on how I approach opportunities and mentorship, and I often recommend it to other young professionals.

Outside of those, I enjoy the occasional mystery novel or personal development book when I’m looking to unwind or get inspired. These reads have stayed with me not just for their content, but for how they made me think differently, and that, to me, is the mark of a good book.