Celina Lee is the CEO and co-founder of  Zindi, the biggest skilled network for data scientists in Africa.

Celina has a passion for unleashing the facility of knowledge for social good. Celina has a proven track record of thought leadership within the intersect between data and development and has played central roles within the launches of worldwide platforms including the Alliance for Financial Inclusion, insight2impact, and now Zindi. Celina’s work has expansively bridged across the private and public sectors and across various development areas including financial inclusion, micro and small enterprise development, market system development, gender, climate change, and public health. She has lived and worked in countries throughout Asia, Latin America, and Sub-Saharan Africa.

What initially attracted you to computer science and applied mathematics?

My entire life I enjoyed math. After I learned concerning the applied mathematics program it just made sense to me because I appreciate how data and math translates into real-world applications. What I like about working with data is that data has a story to inform. Data might be tremendously impactful, but only in the event you get it into the precise person’s hands. It’s magic.

What are a few of the unique challenges of implementing data science and machine learning solutions in Africa?

A challenge is that datasets might be sparse. For instance in the event you are working on natural language processing problems on local African languages, some languages only have 1000’s of native speakers; some are usually not even written. You haven’t got the plethora of knowledge that you just do for English for instance. But the character of the challenge is strictly what makes the solutions much more necessary and impactful.

When did you initially conceive of the concept behind crowdsourcing data solutions?

I learned about Kaggle a few years ago once I was in San Francisco, when it was only a start-up. The concept of getting the gang construct data solutions for organizations resonated with me. But I saw a niche in that the datasets and problems were clearly sourced from large, mostly-American corporate corporations and the participants similarly were mostly from the “developed” world. I had worked for a few years in data within the international development sector. I saw a chance for crowd-solving problems for, and by, other regions as well.

In the primary few days of launching, the platform crashed because Zindi had so many sign ups. Were you in any respect surprised by how quickly this was adopted by the community?

I used to be surprised, but not shocked. We had clearly not anticipated the quantity of traffic we’d get in the primary few days or else it could not have crashed! But I knew that there was a requirement out there amongst young African data scientists and aspiring data scientists for this sort of platform. Young people on the continent are ambitious, energetic, and revolutionary. They may put the work in, and they’re going to make anything possible. So I used to be not shocked that a web based space like Zindi immediately resonated. On Zindi they can connect with other like-minded people from across Africa and around the globe, they will construct recent skills, they grow their very own profiles and portfolio, and so they can get jobs. Moreover, I’d note that individuals took lots of pride within the incontrovertible fact that this was an African platform hosting African datasets and problems. As one data scientist told me, on Zindi she has found a house.

DeepMind launched a contest on the platform a bit over a 12 months ago, what was this competition?

The DeepMind competition was to develop deep learning models to discover sea turtles using the unique patterns on their faces. The geometric patterns on sea turtles’ faces are like fingerprints. But there is just not a considerable amount of close-up and out-of-water images of sea turtle faces. We worked with Local Ocean Conservation, an area non-profit organization in Kenya, that had a group of 1000’s of images collected over 10 years of working in the sector of sea turtle conservation.

The importance of those AI models is they will eliminate the necessity for physical tags, which might be expensive, unreliable (because they fall off or get damaged), and so they might be dangerous to the ocean turtles’ health. We had over 700 participants working on this problem. And the solutions are open-source, and other non-profits are currently working to develop mobile-based applications using the resulting algorithms.

What are some examples of other challenges which have been launched on the platform?

We have now run over 300 challenges on the Zindi platform. These challenges range across many various industries, technical areas, and complexity! What’s exciting is that they’re all real-world applications of AI and data science, mostly in Africa.

To call a couple of: Using machine learning to forecast air pollution levels in Kampala, predicting the energy consumption levels of 5G networks, identifying landslides using satellite imagery, correcting irregular and faulty GPS locations for a fitness app in Egypt, identifying agriculture-related words in Luganda (an area language in Uganda) on the radio, measuring biomass in Ivory Coast using satellite data.

The list goes on! You may check all of them out here.

On average what number of data scientists work on a listed problem, and the way successful are corporations in solving the challenges which are listed?

Often between 500 and 1000, or sometimes more, will work on any given problem on the platform. This will depend on the complexity of the issue and the quantity of prize money on offer. We have now given out a complete of over $500,000 USD to winning data scientists within the Zindi community.

We have now had quite a few success stories through the years. For instance, Zimnat the biggest insurance company in Zimbabwe sourced machine learning algorithms they got from their Zindi competition to predict which customers were most certainly to churn (stop paying and leave the system). They incorporated these models into their customer support dashboard, which enabled them to cut back customer churn by 30% that 12 months! Zimnat also ended up hiring one in all the highest data scientists in Zimbabwe.

Firms own the IP from the highest three solutions. Apart from the models themselves, corporations really value having tons of of intelligent people working on their problems. It’s a strategy to test recent ideas, outsource problems that their internal teams haven’t got time or the technical capability to work on, or often what’s Most worthy is just having an injection of latest ideas and perspectives.

Are you able to discuss how Zindi then connects data scientists with corporations after the competition is over?

There are a complete of 70,000 users (data and AI practitioners) registered on Zindi from across 190 countries on the planet, and 52 out of the 54 countries in Africa. Roughly 50% of our users are in university; 85% have a university degree or are working towards one, and 28% are women. Our goal is to make AI and data science accessible to everyone.

Every month roughly 6,000 are lively on the platform. Which means they’re either entering and dealing on competitions, reading learning blogs, messaging on the discussion forums, direct messaging with friends, or applying for jobs.

Everytime an information scientist enters a contest, posts on the discussion forum, or joins a team, this activity gets added to their Zindi profile. The Zindi profile becomes their live resume and their proof of labor.

We help corporations hire data scientists and construct their talent pipeline in multiple ways. We provide corporations corporate memberships to Zindi, which permit them  to access advantages including running competitions on Zindi where they own the IP of the highest three solutions and additionally they get to rent directly from the leaderboard of their competition. Additionally they get an account to Zindi Talent Search, which allows potential employers to look the Zindi profiles and directly discover and hire candidates based on their actual performance on several types of real-world problems, i.e. the competitions.

What’s your vision for the long run of Zindi?

My vision for the long run is for Zindi to be recognized as the one most significant pipeline of hundreds of thousands of undiscovered and diverse data and AI talent from around the globe. Every aspiring data and AI practitioner will know that they need to come to Zindi. The Zindi platform is a spot where irrespective of their background, they know they will construct their skills, connect with mentors and peers to assist them on their journey, create a profile that showcases their capabilities, and offers them profession opportunities.

And each company will need their Zindi membership with a purpose to stay ahead of the competition because in a couple of years’ time, every company shall be competing on the standard of their data science and AI capabilities.

We currently make a promise to all Zindians on the platform, that we are going to change their life in the event that they allow us to. We have now already seen many young individuals who have began on Zindi, struggling to even load their CSV file, and one to 2 years later after entering multiple competitions on Zindi, engaging on the discussion forums, and teaming up with different people, they land incredible jobs due to the talents and status they built on Zindi.