Day in the life of an analyst

Day in the life of an analyst


09.08.2023 - 11:31

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Hear from Canberra-based analyst, Edward Smyth, about what a regular day looks like working at Mandala. If you're interested in working on policy, economics and strategy problems - apply for our graduate program now.

What does a typical day look like at Mandala? Ed Smyth shares a breakdown of how a day looks in the Canberra office.

Ed works on a variety of projects from tech, sustainability, industry policy and more. He brings a keen passion for data analysis and modelling as well as a drive to influence policy outcomes to his work.

Read on to find out what a typical day could look like for Ed...

9 AM

I get into the office around 9 and get ready for the daily project check-in. These are a 15-minute catch-up to align on what everyone is up to and to see how we can help each other.

At Mandala we tend to work on one main project and help on others part time. My main current project is a research piece for a not-for-profit looking at employment outcomes for First Nations peoples.

9:15 AM

After catching up on emails and the news, my task for the day is to look at the industries of employment of First Nations peoples and to develop an understanding of how regional, age and gender variation will intersect with industries of employment.

I will do this by combining ABS data with proprietary job advertisement data and insights from academic literature to write up our briefing for the client.

11:30 AM

A colleague and I visit a stakeholder to present the high-level insights on our previous project related to electric vehicles.

The project looked at the impact of fuel efficiency standards on the market for new vehicles and estimated the net benefits for consumers and the environment. As one of the modellers on the project, I get the opportunity to present our findings to the team.

12 AM

I have lunch with the rest of our Canberra team while participating in an online, firm-wide Learning and Development session on game theory and its implications for policy.

1 PM

A colleague and I meet to discuss the methodology for a new research project. The project will assess the impact of generative AI on the labour market.

1:30 PM

I get stuck into some data analysis for my current project.

3:30 PM

Time to meet with my project team for a problem-solving session.

These are twice weekly meetings with a project team in where the team brings the issues they are having in their analysis for discussion.

Today’s session focuses on explaining some of our findings about First Nations employment. These sessions always centre on the most difficult questions economics/regulatory/policy questions around and are an excellent opportunity to learn and contribute.

5:30 PM

Home for the day


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