How AI Happens Johnson & Johnson Sr. Director Data Science Curren Katz
Curren is a curious, driven, and creative leader with vast experience in data science and AI. Her original background was in neuroscience and cognitive neuroscience but entered the industry when she realized how much she enjoyed programming, maths, and statistics. Additionally, her biology background gave her an advantage, making her a perfect fit for managing the neuroscience portfolio for Johnson & Johnson. In our conversation with Curren, we learn about her professional background, how her biology background is an advantage, and what she enjoys most about data science, as well as the important work she does at Johnson & Johnson. We then talk about AI in the pharmaceutical industry, how it is used, what it is used for, the benefits of AI both to the company and patients, and her approach to tackling data science problems. She also tells us what it was like moving into a leadership role and shares some advice for people wanting to take the plunge into leadership.
Key Points From This Episode:
- Curren’s professional background and how she ended up in her role at Johnson & Johnson.
- The connection between traditional neuroscience and neural networks in AI.
- Ways in which traditional scientific education in neurology informs AI.
- How much we currently understand about human learning.
- Curren explains her role and responsibilities in her position at Johnson & Johnson.
- What the term ‘precision’ means in her line of work and examples.
- Outline of Curren’s approach to data science and her role at Johnson & Johnson.
- We find out what Curren’s definition of success is.
- The significant benefits of optimizing processes and procedures.
- Curren outlines the various ways AI is deployed at Johnson & Johnson.
- Her experience moving from an individual contributor role into a leadership role.
- Advice Curren has for people who are considering entering a leadership role.
- The importance of trusting your team as a leader.
Tweetables:
“Finding new ways to use data to drive diagnosis is a big focus for us.” — @CurrenKatz [0:11:56]
“In data science, it can be challenging to define success. But choosing the right problem to solve can make that a lot easier.” — @CurrenKatz [0:15:27]
“I want the best data scientists in the world and to have those people on my team or the best managers in the world. I just need to give them the space to be successful.” — @CurrenKatz [0:23:55]
Links Mentioned in Today’s Episode: