How to Hire - Data Scientist vs Machine Learning Engineer
I am pretty sure you landed here because you, in a way or another, asked yourself, what is the difference between a machine learning engineer and a data scientist and most probably Googled “machine learning engineer vs. data scientist”
I recently discussed with my team, and after the debate and hearing the arguments from both parties, Mr. Google.com, and the team, I thought of producing a detailed map of where the cacophony is most magnificent.
Practically speaking, the difference between a Data scientist and a Machine learning engineer differs from businesses to business and its investment in human resources, and this led to machine learning engineers facing generic algorithms and theory questions about Descriptive Statistics and Probability Bayesian inference in interviews.
In the past, we worked with top researchers (Ph.D.) with zero SQL skills, engaged in product development of a BI tool and at the mercy of data engineers to provide the datasets or even to perform basic operations on those data-sets.
Being in the industry for the past 34 years and educating businesses on leveraging technology in day-to-day operations, we observed that recruiters and HR department sometimes uses Data Scientists, Business Analyst, BI Analyst, Machine Learning terms interchangeably and there is no difference in a job posting of a data scientist and machine learning engineer.
The purpose of this article is to share our hiring experience and to help our readers sort through the clutter misinformation and see data science in a whole new brighter, clearer light.
The primary reason for the confusion surrounding the difference between a machine learning engineer and a data scientist is because they are both comparatively new to other computer technologies.
Another cause of confusion that stems from the one I just mentioned comes from H.R., recruiters, and hiring managers, who reasonably can become dazed with the bombardment of new terms and buzzwords hovering around.
This causes them to label job positions inaccurately, often seeming like they are choosing them on a whim. One H.R. representative may call a job position data analytics specialist when in fact they need a data analyst another may employee a junior data scientist when they require a business intelligence analyst, Of course, there are many companies that word their job offers brilliantly but this is not standard across the board which can cause even more of a mess now.
You could search the Internet for different glossaries, but why waste time when we have compiled all the information for you including the infographic.
In the document below you will find an aggregated concise and to the point structure containing all technical and business terms that are frequently used in the field of data science.
We begin by clarifying the similarities and differences between the terms of business analytics, data science, business intelligence, and machine learning.
Then we focus on helping you digest the definitions you need to know in an effective way so that you can suggest correct titles, understand the difference between job description of a data scientist and a machine learning engineers, their qualifications, technical skills and provide you template on how to rate the skill level by asking the questions.
Let’s get straight in.
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