Invitation for data scientists to support development of network and security product

Tender description

The core idea behind the innovation project is to develop our current innovation Martina (in Proof Of Concept (POC) currently), that uses artificial intelligence to predict, and in many cases remediate, network faults and security breaches before they occur to a standalone product. To achieve this, data scientists will be required to develop the models that create the relationships between network faults and cyber breaches. Lot 1: Further details of the requirements and scope will be provided on completion of a Non-Disclosure Agreement (NDA) agreed and signed by both Xalient and the applicant. We require specialist data scientist expertise to support and drive our productisation of Martina. We have identified the gap in skills to deliver bulk data transformation, building machine learning models at scale incorporating supervised learning and unsupervised learning, data trend analysis and data pattern correlation. These are specialist data science and big data skills that Xalient does not possess. The specific knowledge and expertise of the data scientists will be used to design and build the data collection, data processing and machine learning functions of Martina, ready for full productisation in the selected cloud environment. Our product works based on a data collector to collect telemetry information and an "AIOps Module" to process and provide the machine learning functionality. We have broken down the specification of the project into stages below. These stages form part of a larger productisation program to bring Martina to market - however the below stages represent the scope of tasks that are pertinent to this application. • Data Collection - Inbound Integration Module - design and build • Data Collection - ETL (Extract, Transform and Load) Process - Design and Build • Data Collection - Outbound integration module • Machine Learning - Pre-Processing Module • Machine Learning - Machine Learning Module • Machine Learning - Data Correlation Module As a result of our POC work, we feel that the skills required to fulfil the stages in this specification would mainly include: • Python programming • REST APIs • JSON • Relational Database Management System (RDBMS) - MySQL preferred • HTML, CSS, JavaScript • React and Bootstrap • Node.js • Flask (Python) Our research and findings have led us to speculate the above skills are relevant, however applicants are permitted to submit alternative tooling or languages provided they are backed by demonstrable domain experience and relevance. Our goal is to take Martina to market in a "production ready" state. Applicants should detail how they will address the following aspects in their submissions: • Scale - how will the solution scale effectively? • Resiliency - how will the solution maintain service continuity in the event of any single discernible failure within the pertinent infrastructure and/or processes? • Supportability - post launch and "go live" of the solution, what documentation and/or support mechanisms will be provided and in what format to ensure a successful transition into BAU?