You probably already know (or at least suspect) that big tech’s hiring algorithms are extremely methodical. These are companies with multi-billion dollar valuations that have to operate at scale. And they still have to try to find the best hire for the job.

With a little more insight into the similarities and differences between software engineering and machine learning, you can take the next step in advancing your computer science career.

In this article, we’ll break down the roles and responsibilities of a DSPM, how you can go about becoming one, the sorts of interview questions you can expect to face if you’re pursuing it as a career, and your prospects as an data science product manager professional.

We’ll break down both data science and software engineering in terms of their goals, the process used to achieve those goals, and skills required to thrive in the job.

The data science engineer is at the center of the big data revolution where every company scrambles to integrate machine learning, deep learning, artificial intelligence, and data-driven decisions.

In the booming field of data science, it’s relevant to check out how much you can really make at an entry level position. Although anyone can search the median and average salaries, we will break it down by factors such as company size, skill set, and cost of living.