Cyber Security Data Scientist

Direct Search Global
Published
June 24, 2022
Location
Singapore, Singapore
Category
Job Type

Description

To build cyber security analytics solutions that fulfill internal business requirements as well as the needs of industry partners and customers, familiarize yourself with Client’s business domain and objectives.

Develop, test, fine-tune, implement, manage, and document production-ready data analytics models that give cyber security insights.

On a daily basis, work with massive amounts of raw, structured, and unstructured data from internet traffic, logs, and other data sources utilizing Apache Spark, MPP DB, NoSQL, Hadoop, Scala, Python, R, Tableau, and other programming languages.

To better understand the requirements of creating, implementing, and productizing models, collaborate with in-house developers, data engineers, big data architects, visualization engineers, and project managers.

Ensure that the analytics models are in good working order and troubleshoot any problems that may arise.

Manage technical data science initiatives and make periodic improvements to data science methodology and processes.

Manage technical data science initiatives and make periodic improvements to data science methodology and processes.

 

Requirements

A bachelor’s degree in statistics, data science, mathematics, computer science, engineering, or a quantitative subject linked to statistics is required.

Minimum of 5 years of data science experience, preferably in the cyber security field, with experience working with security logs and network data.

Machine learning, experimental design, assessment, and optimization are all areas where I have experience and competence in probability and statistical modeling.

Scala, Python, R, Java, Spark, and SQL are just a few of the languages you should know.

Ability to use visualization and dashboarding tools like Tableau to accomplish rapid prototyping and proof of concept.

TensorFlow, Keras, Caffe, MxNet, Spark, Hadoop, R, and pandas are examples of machine learning and deep learning frameworks that have been used to build projects.

Strong technical background with real-world expertise envisioning, creating, executing, and deploying statistical or machine learning models in a big data context

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