Join us as a Cybersecurity Junior, Data Science on our Cyber Engineering team in Beer-Sheba to do the best work of your career and make a profound social impact by protecting our digital world.
What youll achieve
As a Cybersecurity Junior, Data Science, you will play a pivotal role in our Cyber Engineering group, responsible for developing innovative data-driven solutions and advanced cybersecurity algorithms. You will leverage your analytical skills and expertise in machine learning to enhance our defense capabilities against evolving cyber threats.
You will:
Work independently and collaboratively with internal and external teams to understand complex cybersecurity challenges, define use-cases, and develop cutting-edge, data-driven AI/ML solutions.
Drive the full model development lifecycle for cybersecurity applications, from data exploration and feature engineering to model training, evaluation, and deployment.
Apply a broad range of statistical and machine learning techniques to large-scale cybersecurity datasets, extracting actionable insights and building predictive models for threat detection, anomaly identification, and risk assessment.
Evaluate the efficacy and performance of developed algorithms rigorously, assessing accuracy, fit, and predictive strength against real-world and simulated cyber data.
Contribute to shaping project scope, communicate analytical insights to technical and non-technical stakeholders, and foster a culture of innovative thinking within the team.
Job ID:R247900.
What youll achieve
As a Cybersecurity Junior, Data Science, you will play a pivotal role in our Cyber Engineering group, responsible for developing innovative data-driven solutions and advanced cybersecurity algorithms. You will leverage your analytical skills and expertise in machine learning to enhance our defense capabilities against evolving cyber threats.
You will:
Work independently and collaboratively with internal and external teams to understand complex cybersecurity challenges, define use-cases, and develop cutting-edge, data-driven AI/ML solutions.
Drive the full model development lifecycle for cybersecurity applications, from data exploration and feature engineering to model training, evaluation, and deployment.
Apply a broad range of statistical and machine learning techniques to large-scale cybersecurity datasets, extracting actionable insights and building predictive models for threat detection, anomaly identification, and risk assessment.
Evaluate the efficacy and performance of developed algorithms rigorously, assessing accuracy, fit, and predictive strength against real-world and simulated cyber data.
Contribute to shaping project scope, communicate analytical insights to technical and non-technical stakeholders, and foster a culture of innovative thinking within the team.
Job ID:R247900.
Requirements:
Essential Requirements
M.Sc. in ML-related disciplines Computer Science, Software Engineering, Information Systems, Industrial Engineering) from leading Universities, 0-1 years after graduation (Sep 24' – Sep 25').
High analytical and problem-solving skills with a proven ability to interpret complex data and draw meaningful conclusions.
Strong experience in data analysis, including data manipulation, cleaning, and statistical interpretation. Proficiency in programming languages, particularly Python, for data manipulation, machine learning, and algorithm development.
In-depth knowledge of Machine Learning models, algorithms, and their underlying principles.
Experience with data science projects, ideally showcasing an innovative approach to problem-solving. Demonstrated experience with the full model development life cycle, including model training, validation, and evaluation metrics.
Desirable Requirements
Familiarity with big data technologies (e.g., Apache Spark, Hadoop) for processing large-scale datasets.
Knowledge of cybersecurity concepts, attack vectors, and defensive strategies.
Essential Requirements
M.Sc. in ML-related disciplines Computer Science, Software Engineering, Information Systems, Industrial Engineering) from leading Universities, 0-1 years after graduation (Sep 24' – Sep 25').
High analytical and problem-solving skills with a proven ability to interpret complex data and draw meaningful conclusions.
Strong experience in data analysis, including data manipulation, cleaning, and statistical interpretation. Proficiency in programming languages, particularly Python, for data manipulation, machine learning, and algorithm development.
In-depth knowledge of Machine Learning models, algorithms, and their underlying principles.
Experience with data science projects, ideally showcasing an innovative approach to problem-solving. Demonstrated experience with the full model development life cycle, including model training, validation, and evaluation metrics.
Desirable Requirements
Familiarity with big data technologies (e.g., Apache Spark, Hadoop) for processing large-scale datasets.
Knowledge of cybersecurity concepts, attack vectors, and defensive strategies.
This position is open to all candidates.
