As our Machine Learning Engineer, Carel Agenbag is leading the charge in developing our capabilities in this exciting new area. Over his time so far, he’s worked on projects that include Client Clustering and Behavioural Analysis, Sentiment engineering, and a Fraud Solution with deep learning. To support the rapid growth in his department, Carel’s team will be growing, and we’re on the hunt for new recruits to join him. We caught up with Carel and asked him to share his thoughts on the machine learning (ML) future at Triggerise (now Tiko) and his experience of working towards a united vision with a lot of autonomy.
Over to Carel.
Does the vision and mission of Triggerise (now Tiko) impact you in how you approach your work every day? Why did you choose to work at Triggerise over somewhere else?
The whole purpose of Triggerise (now Tiko) is to provide healthcare for African people, and knowing that gives me inspiration; our fundamental purpose drives me to do better work every day. Knowing that what I do supports people is very fulfilling.
I would also like to add that the work culture at Triggerise (now Tiko) is one of the best I have experienced in my career. The freedom to deep dive into unknown solutions without the fear of being ‘shot down’ is a great feeling. The only way to learn is to fall down and stand up again, and I feel that is a big part of Triggerise’s culture.
Can you tell us more about the development of machine learning at Triggerise (now Tiko)? What are the primary purposes behind it?
The ML capability at Triggerise (now Tiko) is still very new, so there are many low-hanging-fruit opportunities that can be solved with basic solutions. The primary purpose at the moment is to develop the ML function at Triggerise to a point where we can provide more advanced insights and analytics on the data that the company is currently using to identify opportunities for improvement. The main goal is to harness ML as a service for Triggerise (now Tiko), so we can resolve those ‘hard-to-answer’ questions and improve the overall experience for all involved in our ecosystems.
How would you encourage prospective ML engineers to consider a role at Triggerise (now Tiko)?
I believe this role will not be an average corporate job. It will push you into the unknown but also put you in a space where you can learn and create any type of ML solution to achieve the desired outcome. I think this is a great opportunity to get the ML function at Triggerise (now Tiko) to a point where it is a centre of excellence.
The space for ML in a non-profit organisation, and the type of data we work with, is very interesting. The ML models that need to be created are not typical, and I find that exciting.
What are the key elements that this ML project will introduce to Triggerise (now Tiko) and our way of working?
The main change that will happen in Triggerise (now Tiko) is that ML will increase the insights into the organisation. The decisions we make when we establish new partnerships and maintain the other products we offer will be checked against the ML solutions. For example, geolocation solutions may suggest a different location for exploration or identify a need for a provider in a particular area. This will guide our partners in their decision-making. Also, some solutions will improve the Tiko user experience by making it easier for them to get help or receive suggestions while using the platform.
If you’re interested in the role or would like to learn more about Triggerise (now Tiko) and what we do, get in touch with Daiane! She’d love to walk and talk things through with you.