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.