Two Things To Consider When Being A Data Science Manager

We are in the age of technology revolution, digital transformation and information explosion. Data is now like air. It’s all around us. According to a report from McKinsey[1], the new digital ecosystems are likely to emerge in place of many traditional industries by 2025. Therefore data science jobs are booming in the recent job market. LinkedIn data[2] also explains this trend:

“Data scientist roles have grown over 650 percent since 2012, but currently 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles — even those you may not expect in sectors like retail and finance — supply of candidates for these roles cannot keep up with demand.”

In this blog, I’d like to share some thoughts that I’ve learned from others as well as my past experience. It is noted that not every industry is the same, not every company is the same and not every data science team is the same. That is why, most of the time, managing data team and projects seem to be very difficult because there are lots of factors need to be considered and optimized.

Here are two aspects to think about:

1. How to manage data science projects

• Lifecycle: Data science project is different from software development…