Cheltenham, England, United Kingdom
Technical leader for big-data science and massive graph analytics, Mentor for Corporate Professional Development (CPD) programmes and Community leader for data science research. Wide range of jobs in UK Government, as a researcher, software engineer, cryptographic evaluator, data scientist, technical leader and Future Technology Officer (FTO), working with wonderful colleagues, building partnerships across continents and delivering “wow factor” impact in my operational domains of application. Professional member of ACM, Special Interest Groups for Knowledge Discovery and Data Mining (SIGKDD) and Management of Data (SIGMOD). Programme Committee member for Government and Industry Track of ACM KDD conference (2010 – Washington, 2011 – San Diego, 2012 – Beijing). Invited Speaker at RSA Conference (San Francisco, 2003).
After 32 years as a Civil Servant, I’m taking a career break to spend more time with family and friends, to get my work-life balance, well-being and mental health in a good place and to take stock. I’ve had a wide range of jobs in UK Government - researcher, software engineer, cryptographic evaluator, data scientist, technical leader, Future Technology Officer and Head of Innovation - working with wonderful colleagues, building partnerships across continents and delivering “wow factor” impact in my domain. I’m currently enrolling in courses, conferences and webinars to refresh my technical skills and to complete the Stanford University Professional Certificate in Artificial Intelligence.
Worked on cyber security anomaly detection problems for rich, complex datasets with extreme class imbalance. Performed substantial data engineering, including developing Extract Transform and Load (ETL) pipelines, defining graph data models for scalable cloud-native databases and performing massive graph analytics on operational data flows. Prototype system demonstrated a scalable cloud-native graph summary store for operational cyber security host-based event data for all UK Government enterprise host computers. Investigated effectiveness of random forests for classifying benign and malicious events in the DARPA OpTC dataset and built a cloud-native graph summary store for the DARPA data on AWS. Reviewed and updated Data Science and Machine Learning (ML) corporate development training programs. Led study groups for Coursera Massive Open Online Courses (MOOCs) from DeepLearning.AI and courses from Stanford University Center for Professional Development (SCPD) Professional Certificate in Artificial Intelligence (AI). Completed XCS229 Machine Learning (ML) courses, leading a cohort of junior staff in study groups.
Technical Authority for £(multi-million) UK Government Defence & Security (D&S) Programme of Research at The Alan Turing Institute. Built close personal relationships with partners in academia, the Alan Turing Institute and other UK Government departments, enabling me to successfully align joint projects across a large portfolio. Evangelist for the D&S Programme, identifying stakeholders, technical mentors and delivery managers for all projects to maximize the benefits and ensuring deconfliction with existing work. Overcame significant obstacles during difficult first year of D&S programme, delivering £(multi-million) programme of research and establishing cross-Government Steering Group to provide oversight and maximize shared benefits. Technical Authority for £(multi-million) programme of Data Science Research at US Labs. Using professional networks developed over 10 years, aligned research and brokered collaborations between US Labs and UK Government teams. Partnering with US Government Agencies strengthened UK-US joint programmes of research and gave UK Goverment access to and influence over much larger US sponsorred research programme.
Provided thought leadership on the requirements for data science analytic platforms and tools within current and future technical architectures. Led working group of technical experts, system engineers and operational analysts to identify canonical data science use cases and existing prototype systems. Built mid-scale operational platform to demonstrate the value of advanced technologies when applied to the highest priority data streams. Pilot system had to demonstrate that it complied with legal and safety requirements, that there were “human-in-the-loop” experts evaluating outputs and that it was isolated from corporate business systems.