This article about Data Scientists at Twitter will help you understand how Data Science is applied at Twitter and how data scientists contribute to the product’s competitive advantage. You will also learn about the many sorts of Data Scientists at Twitter and their roles and responsibilities.
What role does data science play at Twitter?
Twitter employs data science in two ways since there are two types of data scientists:
Data scientists of Type A
The letter A stands for “analysis” in this context. This is a more static data analysis or insight extraction strategy. A Type, A data scientist’s job is more closely similar to that of a statistician. Data cleaning, dealing with huge data sets, data visualization, domain expertise, etc., are all skills that a Type A data scientist is proficient in.
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Data scientists of Type B
Here, “B” refers to “building.” Type B data scientists have a similar background in statistics to Type A data scientists, but they also have a strong understanding of program and software engineering principles. Direct communication with users is their responsibility.
This aids them in developing products that offer user recommendations and other interactive outcomes.
Responsibilities of Data scientists at Twitter
Constructing data pipelines
Data pipelines are widely used on Twitter. A data pipeline makes it easier for the data scientist to perform operations on the data by allowing data aggregation from multiple sources.
Twitter utilizes these data pipelines for analysis. It facilitates the automatic execution of procedures and drives dashboards to make data consumption easier for consumers.
Experimental Procedures (A/B Testing)
Another important role for a data scientist at Twitter is A/B testing. A/B testing, in its most basic form, is a randomized experiment with two versions. It is a sort of hypothesis testing that allows the company to determine the version that attracts the most customers.
Twitter’s Duck Duck Goose tool includes A/B testing as one of its trials (DDG).
It allows the system to capture massive amounts of data, recognize changes in the social graph, generate server logs, and track user interactions via web and mobile clients by analyzing millions of tweets.
One of the most crucial tasks of a data scientist is predictive modeling and machine learning. Twitter is a playground for data. An enormous volume of data can be utilized through various machine learning and predictive modeling techniques.
Twitter data scientists can apply machine learning to reduce the amount of spam that users get. It also applied advanced deep learning methods to send pertinent notifications.
Gaining Knowledge from the Product
One of the data scientist’s primary responsibilities is to use data to uncover insights and then apply those findings to improve the product. This data is collected every time a user interacts with the device and eventually preserved in a log file or metadata for later use.
This information can be analyzed in a number of ways. The first technique is to use push notifications to determine user eligibility simply. The evaluation of numerous user accounts follows an examination of SMS delivery rates across multiple carriers.
So these were the main responsibilities of a data scientist at Twitter. Generally these duties are the same in all companies. I hope this article gave you a quick insight into what a data scientist does at leading companies like Twitter.