Key Takeaways from Chat
- Transitioning to industry from academia often depends on skills like the ability to explain how you would approach unseen problems and the willingness to learn new skills.
- There are interesting problems that need to be solved in the industry.
- When applying for data science jobs, check when the market is good and be prepared for code interviews.
- Practice and improve how you communicate complex concepts to different stakeholders.
- Bioinformatics and data science are similar in terms of languages used (Python), statistical and mathematical skills, using the command line, and the scope of the problem.
About the Speaker
Emily is a Principal Data Scientist at Suncorp Australia, leading a team of 10 talented data scientists within Suncorp’s Data Science and AI Centre of Excellence. Her team collaborates with various business units to develop and implement innovative solutions that drive operational efficiency. Currently, she focuses on utilising Large Language Models (LLMs) to process and analyse the vast amounts of unstructured data that pour into Suncorp, particularly through the insurance claims process.
Before entering the corporate world, Emily earned a PhD in Mathematical Physics and spent several years in academia, including postdoctoral research in Bioinformatics. Transitioning into the financial sector from bioinformatics research has given her a unique perspective on how data science techniques can differ depending on the industry and how they can be adapted to tackle a range of challenges.
In this fireside chat, she discussed the techniques data scientists use to approach complex problems, comparing them with those commonly employed in bioinformatics. She also offered a glimpse into daily life for a data scientist working in a major financial institution like Suncorp, where the fast pace and high stakes make for a dynamic and exciting work environment.
Emily is happy to respond to LinkedIn messages and emails from students.