Cyber Data Scientist

Overview

This role is responsible for developing and deploying analytical models on data and processes. They collaborate with digital product teams to deliver insights to stakeholders, drive automation of processes, and help improve decisions. This position will drive deployment of a variety of models and workflows using cloud-based model orchestration and data virtualization solutions. The role will require exploration and adoption of new technologies made available by IT engineering and cybersecurity teams. It will require close partnerships with other internal teams to shape the solutions as they are developed and to assist in enabling and migrating production workflows. Responsibilities ·Develop and deploy analytical models on data and processes to generate insights, support automation, and improve decision-making. ·Collaborate with digital product teams and internal stakeholders to shape, enable, and migrate production workflows. ·Apply statistics, artificial intelligence, and machine learning techniques to large data sets. ·Use multiple programming languages and data transformation tools to support model development and analysis. ·Work with databases, data structures, and data architectures to support scalable analytical solutions. · Create visualizations, presentations, and clear quantitative summaries for technical and non-technical audiences. · Explore and adopt new technologies provided by IT engineering and cybersecurity teams. · Work independently with limited supervision while maintaining strong attention to detail and effective team collaboration. · Mentor junior cyber data scientists. Educational Requirements / Qualifications ·Bachelor’s degree and six (6) years of experience in the data science field; four additional years of related experience may substitute for the degree. ·Proficiency in mathematical modeling, including theory, modeling and identification strategies, and limitations or pitfalls. ·Experience with relational database management systems and open-source frameworks. ·Experience with machine learning or artificial intelligence. ·Familiarity with the Risk Management Framework (RMF) process.