If you are an experienced and passionate Data Engineer, this is the opportunity for you.
Excellent career opportunity to gain experience in quantitative data analytics or machine learning data model development for Cybersecurity / Network Infrastructure.
Be at the forefront of innovation working in a high performance and fun team.
- Bachelors in Computer Science or Computer Engineering or Information Technology or Mathematics or Statistics or Data Science;
- At least 2 years of working experience in quantitative data analytics or machine learning data models;
- Experience using open source data science tools such as R, Python, TensorFlow and GPU computing to deploy and manipulate large data sets;
- Experience in solutions using technologies such as Hadoop / Hive / Hbase / Greenplum / NoSQL;
- Deep knowledge of Analytics model/software and tools with the ability to conceptualize, architect the key components and to develop prototypes using statistical software packages such as R / SAS / SPSS;
- Extensive experience working with very large data sets, data visualization, data mining, and data cleansing/transformation and machine learning;
- Proven track record of solving challenging problems by applying advanced analytics;
- Good analytical ability, conceptual thinking, planning and organisational skills.
- Good understanding of Artificial Intelligence, Machine Learning and Deep Learning concepts.
- Drive and conceptualise the new programs or initiatives related to the use of big data to improve cybersecurity;
- Architect the overall data analytics platform to support the deployment of the analytics models, applications and visualization of the results;
- Build analytics tool that uses data pipeline to provide actionable insights to the team on big data related issues;
- Design and implement data pipelines (real-time and batch) to incorporate new big data sets;
- Applying feature transformations for machine learning models on new data sets;
- Identify cybersecurity use cases and develop data analytics rules or data models to deliver the use case outcomes;
- Continuously, cleansing, verifying, monitoring and testing the system to ensure optimized performance of the models;
- Work with vendors to develop data analytics systems and validate accuracy of data models developed.