Meet Joseph: from Chocolate Engineer to Data Analyst at EY

What were you doing before you were at Le Wagon?

Before Le Wagon I was actually working as a “chocolate engineer”… kind of!

I studied chemical engineering at University, and straight after, I joined Mars Wrigley confectionery as a graduate. I worked on the production engineering team, and my main job was actually working on the Maltesers production process. I would try to find ways to improve the process line, and ran production line trials for new Maltesers product variants.

Working in a chocolate factory might sound like a dream job. And in a lot of ways it was… I loved my job. But I was also drawn to tech. I kept hearing the phrases “Machine Learning” and “Artificial Intelligence” thrown about so I decided to look more into it. I had read that the first step into data was to learn Python. So I did a few free beginner python courses online to see if I would like programming – and I was hooked!

Once I knew that working with data was something I wanted to commit to, I started looking around for ways to accelerate my journey into the field. I came across Le Wagon, and it seemed to be a perfect solution. I chose the full-time Data Science course, intense and challenging, with good reviews and good employment outcomes. I even had one of my friends (who is a Data Scientist) chat to the Le Wagon staff to ask them more technical questions – a vetting process of sorts! My friend’s “tick-of-approval” was the last thing I needed to quit my job at Mars and join Le Wagon’s Data Science course.

How was your experience with the course?

I’m happy to say that I had an extremely positive experience with the course. The first thing that stood out to me was how great the teachers were. They were really approachable, so any time I had questions or was uncertain about something, they were happy to answer and explain things in a really simple way.

A component I really enjoyed learning about was the application of the Machine Learning models. We used the scikit-learn Python library quite extensively, and got to apply it in some really interesting ways. One of my favourite exercises was using Machine Learning to predict whether or not a heart was “at risk” based on echocardiogram data. It was really cool to see how Machine Learning can be trained to identify complex patterns that would otherwise take a cardiologist-in-training many months to properly recognize.

This knowledge I picked up in the course definitely came in handy for the final project. Our vision was to use Twitter to make cryptocurrency price predictions. My role was to collect data and develop a Machine Learning (ML) pipeline that could make these predictions. I scripted a process for pulling data from Twitter and a cryptocurrency price website by consuming their APIs. Then, using Python, I created an ML pipeline that trained a model using this data and ultimately deployed it to the cloud. Although our model wasn’t as accurate as we had hoped, it was really empowering to know that we could take an idea and build it into something real, even though it wasn’t perfect.

I also really liked how the end goal of the course was to produce something tangible: a ML/AI powered web application. The entire course was structured in a way to give me the skills to be able to achieve this goal. Yes, the material was challenging, but I loved challenging my mind, and learning new concepts.

The post bootcamp experience has been incredible – I had great career support at the end of the course, and have formed friendships with my fellow students and other alumni that I treasure to this day.

What are you doing now?

I’m actually keeping very busy at the moment. I work full-time as a Data Analyst/ Data Scientist in the Data and Analytics team at EY Melbourne. Working in consulting has been great, being exposed to a number of different industries and also working on a lot of different things. In the year that I’ve been with EY, I’ve worked on everything from data analytics and dashboarding, to doing data migrations, to doing data science and MLOps on the cloud through Microsoft Azure.

I really love the data culture at EY, they really do a lot to try and promote data science development within the company. For example, they have a “Data Science” club, and they also host an annual “Open Science Data Challenge”.

I also volunteer with an organization called the “Good Data Institute”, or GDI. This is an organization comprised of other data professionals like myself (Data Scientists, Data Engineers, Data Analysts, etc.), and we aim to help not-for-profits with their data needs. I’m currently working with an organization based out of New Zealand that aims to support those trying to find employment and/ or obtain their driver’s licenses.

Thank you, Joseph!