NB: This interview was recorded in May 2021. (Audio Transcript)
In this episode, I had the pleasure to talk to @Jillian Augustine, Ph.D.! Jillian Augustine, is a British-Grenadian data scientist living in Vienna, Austria. She was born and grew up in West Yorkshire in the north of England whilst also (thankfully) spending many school summer vacations on Carriacou, Grenada. Jillian received a Bachelor's degree from the University of Leeds, in the UK during which she also spent time at McGill University, Canada. In 2018 she received a doctorate in Molecular Biology from the University of Vienna. Since then she has been working in a range of large and mid-sized companies in various AI/digitalization teams and is currently a Senior Data Scientist in the AI Centre of Excellence at Crayon.Austrian AI Podcast | Episode 3
Making the career jump from academia to industry and the intrinsic value of inclusiveness
On the show today, I have the pleasure to talk to Jillian Augustine. Jillian is currently working as a Data Scientist in the Digital Excellence Team of the international paper and packaging manufacturer Mondi, but has received your Phd in Molecular Biology. On the show we are discussing Jillian experience working in academia and private industry. She is contrasting the mindset prominent on both sides, and what is needed to be successful, as well what industry can learn from academia, and vice versa. In addition we briefly discuss different ways how domain experts and data scientists work together most efficiently, and how improvements of tooling and software might change this relationship in the future. We end this episode, talking about diversity and inclusiveness. Jillian shares with us, what it means, and how it feels to be part of an underrepresented group. The importance of role models, and the intrinsic value of inclusiveness.Women in Data Science CEE 2021 | 8th March 2021
Moving in to Data Science from Non-CS Academia (Audio Transcript)
Working in academia provides a solid foundation on which you can build data science skills. But if you are outside of an advanced mathematical or informatics field, knowing exactly where your strengths lie and what you need to work on can be unclear. In this talk I will answer questions from an open survey of people who want to transition into data science. I will talk about the requirements for your first data science job, what hiring managers look out for and my personal learning path.MONDI & WeAreDevelopers | November 2019
the art, science and maths of doing data science
A short promotion of Mondi's paricipation in WeAreDevelopers 2019.Big Data LDN 2019 | 13th November 2019
DATA SKILLS: HOW MONDI DOES DIGITAL
Talk Description: Organisations are becoming more aware of the importance of data in their daily business. But hiring people with the right skills is only part of the process. Building a Digital Transformation team in a traditional, multinational industry comes with its own challenges. How do you integrate specialists into teams across the globe whilst ensuring they stay connected and work as one unit? In this talk I will introduce our Digital Transformation team, our extended data community and how Mondi does digital.Women Techmakers Vienna 2019 | 9th March 2019
From Test Tube to Data Lake
Talk Description: When I tell people where I work and what I studied, the response is always one of surprise. If you were to say that molecular biology research and telecommunications data science have nothing to do with each other, you’d be right… but also wrong. In my talk I will share with you my take on careers and answer some of the career-related questions that are often posed to me on this topic. I will talk about my transition from academia in the life sciences, into the telecommunications industry, and my experiences along the way. I will introduce you to the world of data science at A1 Telekom Austria, how we manage and protect your data, and how we use it to improve the user experience of our customers. Using examples from my current day-to-day work, I will show you how the two worlds are more similar than you might think.