I’m a multidisciplinary Analyst and Research Specialist starting a new path in Data Science. From my career and academic history, I’m strong with data, understand the place of information in decision-making, and have experience communicating results effectively. Through a 12-week immersive course at General Assembly, I've been able to apply the latest tools and algorithms in the Data Science field to massively expand my skillset in transforming data into knowledge. Passionate about making performance, products and processes better, I'm seeking a project-based or goal-oriented role which allows development of my Data Science toolkit while having direct impact on the end application.
Full time immersive student in the Data Science programme including over 420 hours of professional training over twelve weeks. Hands-on application of statistical regression, machine learning and natural language processing across both individual and group projects, including presentation of results to technical and non-technical audiences.
Service Director and Unit Leader of a cross-sector and multinational customer portfolio, leading team to achieve on-time delivery of digital workflow, reporting and analytics solutions, software implementation and training deployment.
Manager and team leader of a large customer portfolio, delivering digital solutions, software implementation and training deployment.
Daily responsibilities involved liaising with developers, prototype reviews and quality assurance, delivering training and demonstrating software, creating quality documentation, and extracting/updating records using SQL.
Managed digital records of health and safety incidents, training and audits, tracking actions to completion. Data analysis and trend reporting for presentation to senior management.
Hands on-experience of qualitative, quantitative and mixed-methods research design and execution. Areas of interest include ethical consumption, alternative housing, citizenship, the informal economy and voter motivation. Graduated with distinction.
Customised study programme of Economics, International Relations, Management, Mathematics, Physics and Oceanography for a mixture of hard/soft and theoretical/applied sciences.
I built a model to scan Terms of Service documents, identifying concerning terms and classifying into topic, to explore how terms can potentially be made easier to understand, recognising that most people agree without reading them. Through Natural Language Processing classifier techniques and a public data scrape, I achieved an accuracy of 75% in predicting unfavourable terms and 67% in predicting topic, against a baseline of 11%. Based on this exploration, I’m currently building a proof-of-concept web app to demonstrate the end-user application, before refining the accuracy further through additional data, model tuning, and re-evaluation of the classification framework.