Landing algorithm I North America data post position analysis


There is a very interesting phenomenon in North American IT companies. Many single coders look for the same company‘s data scientist as their favorite partner. Why?Because big data research shows that data scientist is a position rich in handsome men and beautiful women~So let’s study what this data scientist post, which is rich in male gods and goddesses, actually does?

Data scientists of large technology companies are generally divided intoInformation, analytics and modeling / algorithms do hypothesis testing and experiment design, data intelligence and product analysis, machine learning and data modeling respectively.

Data scientist in large IT companies has a clear division of labor, especially in general hiring companies. Generally, a job focuses on one or two of information, analytics and modeling.For companies like Microsoft and flax that recruit by group, the specific responsibilities of DS will vary greatly according to the corresponding group. Ds of other industries, retail companies or startup, is more full stack and needs to do everything; The pharmaceutical industry focuses on information; The financial industry focuses on modeling. Let’s talk about the division of data scientist, data analyst, machine learning engineer, Data Engineer and research scientist by each big IT giant.

Google:Google once had a position called quantitative analyst, or QA, which is their hard core data scientist. Maybe I think QA is too unattractive, so I add the famous prefix data scientist. Google also has a kind of data scientist called data scientist, product analytics.Half of Google’s data scientist posts are QA and half Pa. HR will confirm with the interviewers during the interview.In addition, there are software engineer machine learning (MLE) and data engineering (DE) related to ml.

Facebook: Data scientist, analytics is product analytics, infra data scientist and core data scientist do more machine learning. The corresponding title of research scientist is PhD scientist. Like Google, there are software engineer, machine learning, or MLE, and Data Engineer related positions in Facebook and ml.

Amazon:Amazon has four types of jobs closely related to data: Business Intelligence Engineer, data scientist, research scientist and applied scientist. Among them, BI Engineer is the combination of analytics and Data Engineer. Their data scientist does some analysis and part of machine learning modeling. The research scientist of a family does ml research, but the coding part is less. It sounds like the most grounded applied scientist is the one with the highest requirements and the most money in their family. It’s very hard core. Don’t be confused by the name applied. This position needs talents with strong ml and coding skills.

AirbnbHis family’s data scientist is famous for track, analytics, information and algorithms. Analytics is product analytics. Information is mainly used for hypothesis testing and experimental design. Algorithms is used for machine learning models.

LinkedIn:LinkedIn’s DS also has three tracks: strategies and insights similar to analytics, information and algorithms similar to information and modeling, and data engineering. LinkedIn also has software engineer machine learning, but it is not on the same org as data scientist. LinkedIn’s ml is very hard core. Basically, it only recruits PhD and does a lot of algorithms.

Uber:Black car’s product analyst is the data scientist of analytics, and the work of data scientist is information and modeling. At the same time, Uber has another MLE.

Lyft: research scientist partial modeling, data scientist partial information and analysis. Their original corresponding positions were analyst and scientist. Later, in line with the industry trend, they changed analyst to data scientist and the original scientist to research scientist.

Apple:Apple is more mixed because it recruits people by group. It’s also data scientist. Some groups do dashboards, while others do machie learning engineers. Some DS and MLE of other companies are the same, so you need to know more about them through job description, HR introduction and interview.

Microsoft: like apple, Microsoft usually recruits people by group, so it’s more mixed, some of them are analytics, some of them are deep learning.

TwitterTwitter’s DS is comprehensive, with algorithms, analytics and inference. During the interview, you can ask: in the three DS sections of analysis, information and ml modeling, what is the proportion of your group’s usual projects in these three fields? Through this question, we can basically understand the nature of their group’s work.

It can be seen that although they are all called data scientist, the scope of DS work of each technology giant is different.Whether you like deep learning with hard core or analytics with communication focus; Whether you are good at designing experiment information or focused on building machine learning model algorithms, you need to know that the spectrum in DS is very extensive. Interview time or each field should be more or less prepared to.Only by giving full play to the advantages of the long board and making up for the shortcomings of the short board, can we quickly go ashore and get a big package in the ever-changing market trend.At the same time, it is also a great challenge for the interviewer’s ability and preparation.