Thank you Jill for taking your time sharing your coding journey.

Jill was introduced to code through storytelling using data during her PhD in Molecular Biology and how she found out about machine learning.

Learning to code has given her more freedom to choose her own path and able her to learn from may different industries.

Read on to see tips for the newbies too.

Please introduce yourself

Hi, I am Jill. I’m a British & Grenadian national living in Vienna, Austria. I’m currently working as a Senior Data Scientist at Crayon’s AI Center of Excellence.

What was your background before learning to code?

Before learning to code I was working towards my doctorate in Molecular Biology. I spent most of my time in the lab working on experiments. Before that, I completed a Bachelor’s degree in Biochemistry at the University of Leeds in the UK, during which I spent a year at McGill University in Montreal, Canada.

What got you interested in coding and how did you learn to code?

My interest in coding came via storytelling using data. During my PhD I spent a lot of time listening to talks about their research and saw how people presented their findings visually. I became interested in recreating visualisations and for this I started by using R and the ggplot2 package. This was my first introduction to coding.

Scientific research is not only about showing data but also about finding out insights and patterns in t he data using statistics. This is how I found out about machine learning algorithms and how to apply them to understand data better.

How did you get your first job in tech?

My first tech job was as a Graduate in AI and Data Analytics at A1 Telekom Austria (Austria’s largest telecommunications company). This job gave me an overall introduction into the business world and was specifically designed in that it required close collaboration with other Graduates with specialisations such as Marketing, and Business Strategy.

I applied for this job with the traditional cover letter and CV. After applying, I had to do a video interview in which I had 30 seconds to read a question and then 3 minutes to record an answer to the question. I had to do this for 7 questions. In the second and final stage of the interview procedure I took part in a 12-hour assessment centre where I was assessed on a range of skills. This included my ability to present a project outline, and a 4-hour observation workshop in which I worked with other candidates (from the other specialisations) to come up with an idea for a new digital product.

How did you prepare for an interview?

I prepared the typical interview questions up front such as “Explain a time when you overcame a hurdle” or “What is your biggest weakness”. Then I also prepared a small machine learning use case using publicly available data as a way to show the interviewers that I could clean and visualise data as well as create simple machine learning models.

How does coding change your life?

Compared to working in biology academia, coding gives me more freedom to choose my path. Coding skills are highly sought after and also highly applicable to many industries. This means I’ve been able to work for three completely different companies and learn things about how those industries work. I like this because I’m very curious about learning not only coding skills but also learning about how companies work and how people interact.

Coding also gives me the freedom to build whatever I want. If there is something digital I want to be able to do, there is a good chance there is open source software available that I can use to do it.

Any obstacles that you have to overcome in learning coding?

Not really. My background in molecular biology research prepared me quite well but mainly on the non-coding side. Things such as documenting code well or being able to debug something quickly are things that I think I learned during my PhD which I then was able to apply easily to coding.

Tips for newbies?

1. You will make mistakes. Everyone makes mistakes and everyone uses Google when they are writing code. You will also crash your computer a lot. This is normal.

2. Find a topic that interests you and build a small data science project based on it. Start with untidy data, clean it, visualise it and use it for some kind of machine learning modelling. Your experience doing this will give you a lot of things to talk about in an interview. Because you have real experience, you can talk about it more freely than if you are trying to show your knowledge of data science from a purely theoretical standpoint.

3. Document your code well, you will forget what it means by the next time you look at it.

4. Don’t copy-paste from StackOverflow ;-) It is good inspiration to write your own code but you should learn what code actually does. Which leads me on to…

5. Read the documentation for whatever language/package you are using. Understand the arguments used in the functions you are writing and check the examples in the documentation. These are usually much better than the StackOverflow examples.

6. For those moving out of academia, any office job outside of academia is better than no job. I say this because sometimes people in academia can think they “deserve” a highly specialised industry position straight away but it doesn’t work like that if you have no business experience.

What are your plans for the future?

I’m enjoying what I do at the moment and I will see where it takes me. Thankfully now that I can code, my options are limitless.

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