How is machine learning and data science linked?
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In short, the key differentiating factor is that machine learning or relevance engineers as they are called at LinkedIn build products and services, while data scientists focus more on analytics and informing strategic and product decisions.
Relevance engineers build products and personalization systems across all product lines at the company and thereby drive value to LinkedIn members and clients, as well as the top business metrics. Examples include Feed Personalization, Ads ranking and delivery, Search ranking, Job recommendations, etc. These recommendation systems are based on a wide range of statistical / machine learning methodologies (generalized linear models, tree based methods, deep nets, latent models, etc.) as well as distributed machine learning infrastructure, indexing and online serving systems, which the teams develop. Representative work also gets published in top conferences such as KDD, WWW, WSDM, etc.
Data scientists at LinkedIn focus heavily on decision making. Data scientists work on deep dive analyses to help guide product strategy decisions, size market opportunities, design experiments, and, generally, answer some fascinating product questions with data.
Overall, both types of roles offer plenty of super exciting challenges in working with LinkedIn’s unique data and business spaces.
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Relevance engineers build products and personalization systems across all product lines at the company and thereby drive value to LinkedIn members and clients, as well as the top business metrics. Examples include Feed Personalization, Ads ranking and delivery, Search ranking, Job recommendations, etc. These recommendation systems are based on a wide range of statistical / machine learning methodologies (generalized linear models, tree based methods, deep nets, latent models, etc.) as well as distributed machine learning infrastructure, indexing and online serving systems, which the teams develop. Representative work also gets published in top conferences such as KDD, WWW, WSDM, etc.
Data scientists at LinkedIn focus heavily on decision making. Data scientists work on deep dive analyses to help guide product strategy decisions, size market opportunities, design experiments, and, generally, answer some fascinating product questions with data.
Overall, both types of roles offer plenty of super exciting challenges in working with LinkedIn’s unique data and business spaces.
HOPE U WILL UNDERSTAND THIS ANSWER
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Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions
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