TAO Data Science Panel

Questions and answers

  • How does your company define data science? How would you describe it to a layperson?

Feels like the early days of synthetic biology: 10 professors giving 11 definitions.

For me, it is the use of modelling methods to:
1. Answering scientific questions at the boundaries of knowledge.
2. Accelerate decision-making in routine research processes.

  • What types of problems do data scientists work on at your organization?

Tons! Here's a sampling of the themes I've seen at work:

  1. Building knowledge graphs
  2. Image search and retrieval
  3. Predictive models of molecule properties
  4. Input design optimization
  • How do you use data science in your role?

It's my full-time job :).

  • How much of your work is independent vs team-oriented?

Our specialties means we are always conducting highly independent research work - as part of a team of scientists who are answering a bigger question.

  • What do you wish you had known when you were going to school/first starting your career in relation to data science?
  • How can those interested in a data science career get access to the tools they need to succeed? What skills are commonly missing in candidates?
  • Is it challenging to get talent in data science? What type of background is most valuable in your data science hires?

Each team looks for a slightly different thing. Some require deep background knowledge. Others require

  • What types of data does your organization work with?
  • What challenges does your organization face that can be solved with data science techniques?
  • Where do you think the field is headed?
  • What technical and soft skills are critical?

In terms of so-called "soft" skills, in my interviews, I look for the ability to translate problems into code, and vice versa. Being able to explain a model's core succinctly enough to another person to be able to implement it in code.

State of Data Science