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