The Benefits People Outside Our Field Bring When They Transition Into Data Science
You can break into Data Science from traditional and non-traditional, even non-technical domains. Why? Transferable capabilities. Here's a list of the advantages I see coming from other domains.
Marketing. Domain knowledge in marketing is a huge advantage. Many Data Scientists understand the methods and approaches but don't have a strong grasp on the business needs.
Marketing touches most of the business, customers, and product that people transitioning into Data Science bring a fresh perspective. They help the team align solutions with value. They are also excellent communicators.
Nursing and Medical. Healthcare has some of the highest value use cases for Machine Learning. It's a complex field, so domain expertise is a critical success factor.
People who have worked in the field bring a deep understanding of the regulatory environment and the reality of how people in the field do their jobs.
Most healthcare Machine Learning solutions don't align with users' workflows. The 1st company to build a regulatory compliant product that works with docs, nurses, CNAs, etc., will have a billion dollar market open up to them.
Sociology and Anthropology. A deep understanding of how people interact is critical for most Data Science use cases. Collaborative decision-making to customer behaviors and community building all benefit from that knowledge.
Data Scientists need to build solutions that support human-machine teaming. People are working with models now, so what Data Scientists build needs to consider how to optimize those interactions.
People from these fields have a sense of the dynamics of human-machine teaming and building trust with users. I can't underemphasize how critical this perspective is for a D&A organization.
Good old math and hard sciences. We need scientists to review our experiments and mathematicians to review the math behind our models. There's no substitute for scientific rigor and methodology.
The more reliant the business becomes on Machine Learning for internal automation and new product, the more reliable those models must be. Hard sciences build models to a higher standard.
Hard scientists help the business implement a research lifecycle. They help set up experimental review and results validation processes. This maturity level is required to support advanced projects.
We also need leaders from across domains. Few data scientists have leadership experience, and most don't want to develop into leadership roles.
Project Managers also make great Data Scientists.
We need people who communicate for impact, lead without authority, build coalitions across the business, and get buy-in from senior leadership.
Ex-Project Managers and Program managers turned Data Scientists can be rock stars.