Meetup #95

Applications of Data Science

How do we effectively integrate and utilize data science in mature engineering and business systems? Connie talks about her experience in how this can be done, drawing from both experiences in Big Tech and leading the data efforts at an early-stage tech startup.

Take-aways

- Understand the different types of data science roles and how those roles can be impactful to the business / product - Understand the skills necessary for each of the various data science roles and how that could influence someone's choice in the type of data scientist they would develop themselves into - Give examples from real-life applications of data science (both successful & unsuccessful) - How to break into the field of data science

In this episode

Connie Yang

Connie Yang

Lead Data Scientist, Pallet

At Pallet, Connie has been leading various data science work streams on integrating machine learning, intelligence, and automation into the entire Pallet product--including creating multi-label, multi-class auto labeling machine learning models deployed as cloud-native APIs to be integrated into Pallet backend, creating holistic and diagnosable health scores for various stakeholders and scaling out Pallet's data collection system.

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Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.