Data Science – Role vs Responsibility

As they say- Data Scientist is the Finest job of the 21st century. However, I must say that the interview process for Data Science is the messiest one of all time. One of the main reasons for not having a clear, concrete or streamlined interview process is the confusion around the job titles vs. responsibilities expected from the role. 

In this article, I will try to unbox what the Data Scientist as a job title means for the top four tech companies- Amazon, Google, Microsoft and Meta (previously known as Facebook). I will also highlight the top skills needed for those roles.

Data Science Job in Google: If you are going for the Data Scientist title, most of them are actually the re-branded titles that used to be called Quantitative Analysts in google. Most of the data scientist roles are aligned to work with product teams, and it is more into the analytics side than the core ML (Machine Learning) side. 

Google has another job title called ML Engineer, which is more aligned with ML focus, and they fall under SWE (Software Engineering). Most of their data science titles have the product team listed in the job title. For example- Data Scientist, Core Compute or Data Scientist, gTech etc. 

Top skills asked are SQL, Python, Stats and Data Analysis skills. If you are more interested in the core ML work (building and deploying ML models), you want to consider ML Engineer titles instead.

Data Science Job in Amazon: The data scientist titles in amazon are a little bit more well defined and structured. I want to give some credit to Amazon for that. For the analytics side, Amazon has other job titles like Data Analysts, Business Analysts and Business Intelligence Engineers. For the core ML, they have Applied Scientist titles. Therefore, the Data Scientist is the perfect balance between the above titles who will have analytics as well as an engineering mindset to build models. Top skills asked are Python, Coding (Data Structures & Algorithms, meeting the SWE level-1 bar), SQL, Data Visualization, Machine Learning concepts (more breadth than depth) and how to build the models. Deployment of models can be additional or preferred skills for data scientists, but for the Applied Scientists both build and deploy skills are required. 

Data Science job in Meta: The data scientist job in Meta is similar to Google, which means they are also embedded with product teams rather than engineering teams. Most of the DS (Data Science) roles in Meta are analytics heavy, working along with product teams to help with data analysis, defining, measuring and monitoring KPIs (Key Performance Indicators). Meta also has ML engineer roles which are focused more in the core ML. 

Top skills asked for DS roles in Meta are SQL, Python, Stats, and Data Visualization. One additional skill they usually demand is experimental design (A/B testing). Therefore the DS interviews in Meta always include one analytical case study problem.

I created the diagram below to distinguish between the two major tracks for Data Science Project roles- one is Analytics side and the other is Core ML side.

Data Science job in Microsoft: One of the most confusing job titles when it comes to Data Scientist is from Microsoft. Being a part of Microsoft myself, I am not super proud of how they have defined the DS roles. Microsoft has the high-level job title to cover most of the data roles under the same title called ‘Data & Applied Scientist’. The good thing is their DS roles are mostly aligned with engineering, so the parity in pay is more comparable to SWE roles. You have to rely on job descriptions to read well and carefully to conclude if the role is more on the analytics side like Data Analyst, BI engineer, or is it more ML focused like ML engineer or Applied Scientist. Sometimes even some Data Engineering roles are defined under the same ‘Data & Applied Scientist’ title. 

Top skills asked are Python, SQL, Stats, Big Data query, and Data Visualization. If the role is focused towards the research or deep learning side- they also ask DL (Deep Learning) frameworks like Pytorch, Keras , Tensorflow etc.

Please follow me on linkedin if you stumble against this article, and find it interesting. In future, I am looking forward to adding more blogs on Data Science career paths. 
My Linkedin: https://www.linkedin.com/in/nirmal-budhathoki

Article Written By Our Advisor

Nirmal Budhathoki

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