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 Nihan:
Nihan Yami is a Data Scientist in the Revenue Operations (RevOps) team at SAS, where she develops AI-driven solutions to enhance business decision-making. With over five years at SAS, she has worked across industries, helping customers leverage analytics and machine learning.
She holds the SAS® Certified Professional: Artificial Intelligence and Machine Learning and AWS Solutions Architect Associate certifications and earned her Bachelor’s degree in Management Information Systems from Boğaziçi University.
How did you start out in your tech career?
I hold a BSc in Management Information Systems, an interdisciplinary field that bridges business needs with technology. I found data science to be one of the most exciting areas to apply this perspective, so I focused on analytics at the beginning of my professional journey. I started in customer-facing roles at international technology vendors, and now I’m in a more hands-on role as a data scientist in our revenue operations team.
What are the signs of success in your field?
Even the most successful model is meaningless if it doesn’t resonate with the business. The real success lies in helping the actual problem owners; whether it’s a marketer, product manager, HR leader, or someone in the C-suite, make better decisions. Focus on the problem first, and work backwards to the technology. Model development is exciting for data enthusiasts, but it should lead to deployment, not just sit in a demo environment.
What is the best and worst thing about your job role?
The best part is the constant learning and it’s nearly impossible to become redundant in this field. We’re also in a time when people of all ages are curious about AI, which makes it fun to explore not just technical, but also societal and philosophical aspects. The downside is that it’s easy to get caught up in technical jargon. I always remind myself to communicate clearly so that even non-technical stakeholders walk away with full understanding.
What can you advise someone just starting out to be successful?
Don’t be afraid to get your hands dirty. “Perfect is the enemy of good”. Once you grasp the basics, choose a problem and try to solve it with data. Present your results, reflect, and improve. You’ll notice progress with every attempt.
How do you switch off?
A quick walk always helps. If I’m staying in, a Turkish coffee with some chocolate is my go-to. The key is stepping away from the screen for a bit. When I return, I usually feel refreshed and more focused.
What advice would you give your younger self?
I’d tell myself not to rush; career growth is a long journey, and you don’t need to learn everything at once. One of the great things about the data science community is how open and supportive it is. There are so many talented people willing to help if you just ask. Build connections and enjoy the journey.
What is next for you?
I’ve always been curious and enjoy diving deep into specific topics. Lately, I’ve become more open to sharing what I learn and exchanging ideas with like-minded people. I’m looking for more ways to do that, whether through content creation, community involvement, or speaking opportunities.
If you could do anything now, what would you do? Why?
Career-wise, I’d focus more on R&D because I love exploring the frontier of new discoveries. The pace of groundbreaking research is inspiring, and I’d love to stay closer to those developments. Personally, I’d remind myself that while reading papers and doing tutorials is fun, making time for rest and recreation is just as important.
What are your top 5 predictions in tech for the next 5 years?
1. AI safety will become a major focus.
2. Quantum technology will move into commercial use.
3. Human-robot interaction (HRI) will gain wider attention.
4. Extended reality (XR) will go mainstream.
5. Autonomous vehicles will become the norm.
Watch Nihan’s session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!