Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Anthonette:
Anthonette Ochieze-Adanyin is a Lead Data Scientist at Ofqual and a published researcher with peer-reviewed work across IEEE and ACM conferences, including presentations at international venues in Spain and Japan. Her work sits at the intersection of privacy-preserving machine learning, bias detection, and ethical AI in high-stakes environments with published results including a 99.2% reduction in sensitive data exposure in production IoT sensor systems. She holds an MSc in Artificial Intelligence from the University of Wolverhampton, awarded with a competitive Office for Students Scholarship, organises PyData Wolverhampton, and mentors across Coding Black Females, BCS, and Reed Women in Technology. She is one of a small number of practitioners working simultaneously across original research, production deployment, and active community leadership in underrepresented spaces in AI.
How did you start out in your tech career?
I started in the banking industry in Nigeria, where I spent 7+ years working on credit risk and Investment management. That period taught me how much data shapes real outcomes for real people. Decisions made in spreadsheets affected whether someone got a loan, whether a small business stayed open, whether a family could send a child to school. I did not realise it at the time, but those years built the foundation for everything I do now.
When I moved to the UK, I had no network in the technology industry and no obvious route into data science. I completed my Master’s at the University of Wolverhampton and slowly rebuilt my career, moving through telecommunications and digital marketing before joining a Uk Civil service arm as its first dedicated data scientist. The path was not linear, but every step added something I now rely on.
What are the signs of success in your field?
Success in data science is rarely about knowing the most. It is about understanding the fundamentals deeply enough to know when something is wrong, when a model is being misused, or when the question being asked is not the one that should be answered. The people I respect most in this field are quick to say “I don’t know” and quicker to go and find out.
Continuous learning is not optional in this work. The tools change, the techniques change, and the problems change. What separates strong practitioners from weaker ones is not how much they know on any given day, but how comfortable they are sitting in the space between knowing and not knowing, and doing the work to close that gap honestly.
What is the best and worst thing about your job role?
The best part is that I get to build systems that shape education with fairness in mind. The work I do affects how qualifications are awarded across the country, which means my decisions have to be technically sound and publicly defensible. There are not many jobs where you can do meaningful technical work inside an institution that is genuinely accountable to the public.
The worst part is the weight that comes with that same accountability. In regulated environments, mistakes are not abstract. A poorly designed system can affect real students, real awarding organisations, and real public trust. I think about that often, and I would rather carry that weight than pretend it is not there.
What can you advise someone just starting out to be successful?
Coding is no longer the barrier it once was. AI can write the how, but it still cannot tell you what to build or why it matters. The people who will thrive in this next phase of technology are the ones who understand the problem deeply enough to direct the tools, not the ones who only know how to use them.
I would also tell anyone starting out that soft skills are becoming the real differentiator. Problem solving, communication, critical thinking, and emotional intelligence are what determine whether you can take a piece of technical work and turn it into something an organisation will actually adopt. Technical skill gets you in the door. Everything else is what keeps you in the room.
How do you switch off?
I dance. It is the one thing that completely takes me out of my own head. No screens, no problems to solve, no decisions to weigh. Just movement and music. Anyone working in high-stakes technical roles needs something that pulls them entirely away from the work, and dancing has always done that for me.
What advice would you give your younger self?
Do not let your race or your background make you smaller than you are. For a long time, I thought of myself as a Black data scientist, as if my identity needed a qualifier. The shift came when I started thinking of myself as a data scientist who happens to be Black. That single change in framing rewired how I walked into rooms, how I spoke, and what I believed I was allowed to ask for.
I wish I had discovered that earlier. So much of what holds people back in this industry is not capability. It is the quiet voice telling them they are an exception, that they need to prove they belong, that they should be grateful to be in the room. That voice is wrong, and the sooner you stop listening to it, the further you go.
What is next for you?
I want to keep using data and AI for public good, particularly in environments where the stakes are real and the people affected are not the ones in the room when decisions get made. Over the next few years, I want to contribute more directly to how responsible AI is implemented inside public institutions, and to keep building the communities and pathways that help more people from non-traditional backgrounds enter this field.
I also want to keep showing up in places where women like me have historically been absent. Not as a token, and not as an exception, but as one of many. Representation matters, but presence with influence matters more.
If you could do anything now, career-wise or personally what would you do? Why?
I would build a research and practice group focused on responsible AI in public institutions, sitting between government, academia, and the regulators working at the frontline of where these systems are actually deployed. There is a real gap between the speed at which AI is being adopted and the maturity of the frameworks meant to govern it, and I think the next decade will be defined by who closes that gap responsibly.
The reason is straightforward. AI will increasingly make decisions that affect access to education, healthcare, housing, justice, and credit. If the people designing those systems do not understand the institutions they operate within, and the people running those institutions do not understand the systems being built, the public will pay for the gap. I want to be part of the group of people working seriously on that problem.
What are your top 5 predictions in tech for the next 5 years?
Regulated AI becomes its own discipline. Building AI systems that survive in regulated environments is a fundamentally different craft from building them in unregulated ones, and the next five years will see that separation harden into a distinct field with its own standards, roles, and career paths.
Agentic systems move from demo to deployment, slowly and unevenly. The hype has run ahead of the reality, but the underlying shift is real. Within five years, multi-agent systems will be doing meaningful work in narrow, well-scoped domains, particularly in document-heavy professions such as law, audit, regulation, and research.
The technical bar shifts upward, not downward. AI assistants make basic coding more accessible, but they raise the floor on what counts as competent work. The premium will move toward people who can design, evaluate, and govern systems, not the ones who can simply write code with help.
Trust infrastructure becomes a market in its own right. Model evaluation, auditing, explainability tooling, and governance platforms will mature into a significant sector, particularly as public institutions begin procuring AI systems at scale and need ways to verify what they are buying.
The talent pipeline becomes the bottleneck. Compute and capital are no longer the scarce inputs. The constraint over the next five years will be people who can translate between technical capability, organisational reality, and public accountability. The countries and companies that invest seriously in that translation layer will pull ahead.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!