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Neil Rankin, CEO of Predictive Insights, is a trained micro-economist with a doctoral degree from the University of Oxford. Neil leads product development and implementation and manages the Predictive Insights team.
Hear what Neil has to say about the advantages of being an economist in the field of data science.
- In your opinion, how does adding economic insights to data science bring value to the outcomes?
- Neil: “Economists generally come with a framework for thinking about social questions, as well as a toolbox of techniques that, I think, are under-used in data science. This includes aspects like thinking about how markets function, or how people may react to different types of incentives. Applied economists spend a lot of time thinking about things like ‘endogeneity’, missing variables, and causality when building models. This thought about the variables or ‘features’ of models can be very important when building robust predictive models and when trying to understand the relationships between input features and predicted outcomes.”
- Can you share examples of how combining economic principles with behavioural insights has been valuable in shaping business strategies or improving operational outcomes?
- Neil: “In my experience, building good models is not enough to change operational outcomes within an organisation. Understanding what an individual or organisation cares about, and the metrics they want to shift is often more important. This is about understanding incentives – something that economists spend a lot of time thinking about. The second key aspect to drive change is understating the types of levers or ‘nudges’ that can help people best use the forecasts or insights. This is where behavioural science helps – knowing what types of biases humans have, and the types of approaches that can be implemented to encourage better decision making.”
- Predictive Insights is partnering with Zindi to host a challenge, aimed to better understand one of the biggest problems, not only in South Africa, but in the world: youth unemployment. Why did you choose to focus the challenge around this particular issue?
- Neil: “There is a lot of evidence that the first few years of a young person’s participation in the labour market is crucial for their subsequent work trajectory. Spending prolonged time without a job when young leads to ‘scarring’ which affects the future ability of a person to find work. It is thus crucial to improve the functioning of the labour market, and the ability of young people to find jobs, if we want to tackle the challenge of unemployment more broadly.”
- Neil: “In choosing this challenge, we want young and aspirant data scientists to think more about these challenges, and we want to see whether they can come up with innovative ideas or explanations for the labour market outcomes we see among young South Africans.”
- As an economist and data scientist, what advice would you give to students or professionals who are interested in pursuing a career at the intersection of economics and data science?
- Neil: “For those with a background in economics, I think it is a great time to be in this space. However, ‘traditionally-trained’ economists often lack specific technical skills which are helpful to have when working in data science. It is important to be able to do analysis in a programmatic language, like R or Python, be on top of processes like version control, through git, and potentially be able to access and clean data, through building ETL (define) pipelines for example.”
Register for the 2023 Zindi Challenge.
Combat Youth Unemployment with Data Science.
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