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Meet Michael Hanks, data scientist

By Social Finance
Published 25 August 2021

What do you do at Social Finance?

I’m a data scientist, so I analyse data to help our partners make decisions. Day to day, I spend a fair chunk of my time on data analysis — building tools and exploring a data set to conduct the analysis in. That’s the bit I get the most enjoyment from. I also spend a lot of time managing stakeholders and collaborating with clients.

Why do you like working for Social Finance?

Before I started this job, I was a business analyst for a private financial organisation. Although the work was interesting, it wasn’t very fulfilling. I wanted a job that was more public facing and had a good cause behind it. Now, I work with local government, charities and decision makers, using data to help people facing disadvantages.

Good data analysis can lead to life-changing decisions being taken. For example, we’ve recently been working with a children’s services team. They wanted to know what the demand for their services could be when schools reopened after lockdown, such as how many social workers they might need. We built a model using data from other local authorities to show what could happen, then helped them to plan for that.

It’s great being able to work with like-minded people. I can’t wait to get back into the office, bump into colleagues and have a random interaction that could spark some new ideas.

But we don’t just take the data and analyse it. At Social Finance, there’s also an emphasis on upskilling the organisation we’re working with. We help them learn how to use data tools, so they can continue their own research and analysis. This could be something as simple as Excel, as many organisations don’t have the resources or latest technology available.

Speaking of tech, what programming languages or database software do you use most often?

The programme language we’re mostly likely to use is Python. I wasn’t actually that familiar with it before I started, but Social Finance paid for me to do online courses so I could develop those skills. We also use R and SQL but if I’m creating tools that will be used by our partners, I’ll probably use Excel or Power Query. We emphasise accessibility over sophistication when it comes to tools our partners can use.

What’s the best part of your job?

I get to work with a lot of nerds! It’s great being able to have discussions about strategies and modelling with like-minded people. I can’t wait to get back into the office, bump into colleagues and have a random interaction that could spark some new ideas.

What project have you enjoyed working on the most?

The projects we work on vary quite a lot, so you get to learn about some really interesting fields: local authorities, palliative care, or growing the housing retrofitting market, for example. I was surprised at how much I enjoyed working with children’s services. It was a rapidly evolving, time-sensitive project, and I felt we were able to collaborate really well both with our partners and internally, as a team.

If you weren’t a data scientist, what was your Plan B?

It still would’ve been something data driven, but maybe in the public sector — like the department of education — or the civil service. I’ve also got a PhD in palaeoanthropology. Although it mostly involved analysing the human fossil record, rather than having adventures like Indiana Jones, but I did go on an excavation in Spain. Excavations are fascinating; everyone should do one at least once in their life.

How or where will data have the biggest social impact in the next few years?

I think it will be in health. The data infrastructure within the NHS is quite mature compared to other organisations, but there’s so much potential for better integration. If our health information was more interconnected, healthcare providers could work better with each other. That’s why there’s a big drive to integrate social care data with healthcare data in the UK.

Giving people and providers better access to health data means it becomes much more public facing and more public serving. Building those systems and building up that data infrastructure is a massive area to improve outcomes in health and social care — two of the areas that people care about the most.

What advice would you give to someone applying for your job?

Being a data scientist for Social Finance is about collaboration. The knowledge and lived experience of public sector organisations is just as valid as our expertise, and it’s important their voices are heard. I’ve learned to approach each project with humility — it’s not just about stepping in and analysing data, but co-developing tools with our clients to help them better understand their own data.

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