Episode Title : What Does It Take To Be A Data Scientist?
Episode Summary: How does one get to be a Vice President of AI at a global semiconductor leader? What professional journey can take from a Ph. D. to that influential role? Patrick takes the audience on a journey from upbringing in Germany, Malaysia, Philippines to education in the UK to an initial job at Los Alamos lab to the CEO of a startup to VP at Samsung. Later on Patrick talks about the skills and experience needed to be a data scientist and the emphasis on communication and business acumen to be a successful data scientist. The episode also discusses where leaders can learn from and what they should be doing continuously.
Journey from Ph. D. to VP (01:30): Patrick grew up in Malaysia and Philippines and went to University in the UK. Luckily Patrick got a job in Los Alamos laboratory. The research work needed a mathematics background so switched to math and started digging deeper into AI. Realized that you can’t apply AI while being a researcher.
AI research Versus Applied AI (05:20): Business case for AI is crucial. Lots of work in research institutions may not be commercially relevant and may not be viable. Having started an AI company, it became imperative to convince businesses that AI can pay.
Skills & Experience to be a Data Scientist (07:40): For a data scientist, programming knowledge in a language like Python is a must. Experience with frameworks like Tensorflow, Pytorch, Keras. (3) Pre-process and data prep (4) Statistical testing & probability analysis. So some mathematical & statistical knowledge along with data wrangling skills. On top of that, there are skills that are less common and more valuable is communication. Should be able to translate from domain english to business english. Talk & Translate.
Importance of Communication(12:00): Data scientists can somewhat easily learn mathematical and programming skills. They find the business communication the hardest.
Where can you learn? (13:30): Learn a great deal from my own department. Learn from my management. Learn from partners and customers. Learn from my own thinking like the Covid testing mentioned earlier. Source of most info personally is LinkedIn.
Future AI engagements (16:20): Currently pushing AutoML. Which models and which parameters. Most of the time people do it by trial and error. We are trying to automate this using AI. The other thing is distributed training. Training takes a long time but using multiple computers we can reduce the training time.
Reality of AI (20:00): AI is a big hype topic. But unlike other hype topics, AI is here to stay. Whatever your role is, get upto speed on AI. If you are a business person, think of ways to use AI.
Resources mentioned in this episode:
Podcast website: https://datatransformerspodcast.com
Data Transformers Podcast
Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.
