In this episode, I'm speaking with Urszula Czerwinska about her path as a data scientist, the projects she worked on, experiences gained as a data scientist, as well as the challenges she's overcome in bringing her machine learning (ML) into production.

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Timestamps:
0:00 - Podcast intro
1:15 - Guest intro and how you got into data science
3:48 - Finding your fit – research or industry and when to transition
7:23 - What types of ML projects do you specialize in
10:41 - ML explainability and interpretability
15:26 - ML explainability with non-technical stakeholders
17:13 - What problems does your team solve within the organization
20:56 - ML in production – how to bring your ML projects from research to production
25:17 - The tools you can't live without
28:11 - Do you have a set process for productizing ML projects
30:08 - Team structures and communication for data science teams
33:42 - Who's in charge of setting up infrastructure for a project and job title discussion
36:29 - Interesting tools and repositories you work with
39:30 - How do you stay up to date
42:00 - Biggest challenges for you in ML
45:12 - Favorite and least favorite thing about being a data scientist
49:52 - Handling a workplace that doesn't understand what a data scientist is
53:07 - Data scientists are 🦄 53:30 Good papers you read recently
58:12 - Tips to improve the data science workflow  

Relevant Links:
- flair: https://github.com/flairNLP/flair
- AllenNLP: https://github.com/allenai/allennlp
- Papers with Code: https://paperswithcode.com/
- Dair.ai newsletter: https://dair.ai/newsletter/
- HuggingFace: https://huggingface.co/blog