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!
Rosaria Silipo, PhD, now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books: “Guide to Intelligent Data Science” (Springer) and “Codeless Deep Learning with KNIME” (Packt).
How did you start out in your career?
It all started with my master thesis. Indeed, in 1992 I completed my master thesis on the usage of neural networks to classify patterns in ECG signals. It sounds like millions of years ago, and it was. Considering that neural networks have now come back in the shape of deep learning, it makes me think that my thesis at the time was very visionary.
What are the signs of success in your field?
The clearest sign of success of a data science project is its deployment. Many (most?) data science projects never reach deployment. They get stuck into the training phase and keep pursuing an impossibly high accuracy value, till the project time and budget is over. So, a data science model reaching the deployment stage is a huge success. It shows that its author(s) could balance technical skills, requirements, communications, and budget in the best way.
Or did you mean the sign of professional success?
This is hard to measure. It depends on personal satisfaction, on intellectual achievements, on great colleagues. It is not just a high salary.
What is the best and worst thing about your job role?
I work as the head of the data science evangelism group at KNIME. As all “heads” I do less technical work than other members of the group or even than my younger self. This, the technical work, I would consider the best part of my job. Being able to deliver a predictive model that does exactly what was required and seeing the awe in the stakeholders eyes is priceless.
The worst part of my job is to set the right expectations. Sometimes you talk to the same stakeholders and you need to explain that no, with that time, and that budget, and that data such and such results cannot be obtained. Sometimes this part of the job requires an incredible amount of patience.
What can you advise someone just starting out to be successful?
Keep your curiosity alive. What you have learned today will become old in the bat of an eye. Keep your interest high in the new technologies and do not be afraid to ask what you do not know.
How do you switch off?
Ah ah ah … I am a huge k-drama fan! To switch off I usually watch a few episodes of my favorite k-drama series … ush ush …
What advice would you give your younger self?
Patience, patience, patience. Sometimes things do not seem to work out, but persevering a bit longer can lead to higher skills for yourself and to more success later on, exactly because of the issues you have encountered and solved.
What is next for you?
In the past years I have built a great data science group. I really hired great people: data scientists, data engineers, statisticians, data visualization experts, and other kinds of data professionals. It has been really a great achievement of mine. The price to pay on my side though was to give up a bit of the technical work. So, in 2023 I would like to reserve some of my time for some special data science projects, possibly a data science for good project. It will be really satisfying to go back into the data science arena.
If you could do anything now, what would you do? Why?
I really would like to start a massive initiative world-wide for data literacy. I think that many (most?) data science and AI techniques are now mature to reach all sorts of professionals than just the initiated data scientists/developers. Domains like medicine, finance, manufacturing, pharma, supply chains, etc … could easily benefit from more or less complex AI techniques. I actually think that the time has arrived to start teaching data literacy as early as high school.
What are your top 5 predictions in tech for the next 10 years?
- Data Engineering as the key to successful AI projects. All the technology that has been developed in the past few years (deep learning, chat GPT, time series predictions) will be standardized, controlled, and included in all sorts of applications, thanks to massive data engineering work.
- Better and more impactful AI models. The improvements in data and data engineering techniques will allow for better and more frequent trainings of new AI models, with better performances, smoother integration, and stronger impact into people’s everyday life.
- More AI as SaaS. Not everybody will be able to train and replicate the success of all AI models. A key to a wider success thus will be to integrate them into SaaS architectures. In this way, everybody can take advantage of predictions made by their own as well as externally trained models.
- Larger Coverage of Data Literacy. AI and machine learning are becoming a set of more and more standardized algorithms and tools. I forecast a larger pool of initiatives for data literacy for all sorts of people, including non-programmers and even high schools.
- The importance of Low Code Tools. For the same reasons, I also forecast a main role played by low code tools for data science and AI. Data professionals of all sorts, and not necessarely just programmers, need to have access to data science and AI techniques. Low code tools are a great opportunity to expand on the data literacy among programmers and not.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!