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Lina Kramer

@linakramer

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Data scientist at King’s College London mapping machine learning in taxation and improving fiscal data quality.

United Kingdom

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At King’s College London, I used natural language processing to review 217 papers on machine learning in taxation studies. I also applied supervised and unsupervised machine learning to fiscal data quality and government revenue trends across 192 countries.

As a Graduate Research and Teaching Assistant, I developed and delivered Introduction to Python in Social Sciences. Before King’s, I managed digitalisation projects for federal government clients at IMTB Consulting GmbH, supporting workflow and document management rollouts, procurement, and staff training.

Experience

Work history, roles, and key accomplishments

Graduate Research and Teaching Assistant

King's College London

Jan 2020 - Jan 2026 (6 years)

Developed and delivered the annual course Introduction to Python in Social Sciences for doctoral researchers across multiple universities. Contributed content on AI's impact on skill requirements to the FutureLearn course AI in Education, and coached small business owners on AI adoption.

Python Teaching AI Machine Learning Data Literacy Course Development

Senior Consultant

IMTB Consulting GmbH

Jan 2014 - Jan 2020 (6 years)

Managed large-scale digitalisation projects for federal government clients, overseeing budgets, risk mitigation, and change management. Supported implementation of document and workflow management systems, prepared agencies for rollout, and delivered training programmes.

Project Management Workflow Optimization System Configuration Training Change Management IT Procurement Mentorship

Education

Degrees, certifications, and relevant coursework

King's College London

PhD, AI and Public Policy Research

2020 -

Activities and societies: Recipient of UKRI ESRC LISS DTP studentship. Winner of 2023 Faculty Three Minute Thesis Competition.

PhD in AI and Public Policy Research, part-time, awaiting viva. Research focused on systematic review of ML in taxation studies and application of supervised and unsupervised ML to fiscal data.