Dr. Lindsey Lynch

One of a three-person global data team supporting 18 businesses across the group. Lead day-to-day reporting functions and single-handedly maintain the majority of dashboarding across finance, operations, and marketing, with hands-on involvement across the full data stack — from warehouse and pipelines through to end-user dashboards that underpin strategic decision-making.

• Platform: Contributing to the maintenance and development of the group's centralised data warehouse and Streamlit dashboard reporting solution, syncing data from multiple NetSuite instances, Shopify, and Google.
• AI: Working AI-assisted daily, using agentic coding tools and prompt engineering to accelerate the build-out of reports, dashboards, and analyses while maintaining rigorous review and quality standards; integrated AI into automated workflows for sentiment analysis of customer feedback.
• Analytics: Applying strong analytical and strategic thinking to workstreams including customer lifetime value and acquisition cost modelling, discount dependency analysis, pricing and shipping margin analysis, and vendor spend reporting — navigating ambiguity and converting changing business requirements into effective plans and solutions.
• Machine Learning: Built a gradient boosting model predicting customer loyalty from first-order baskets, scoring new customers so marketing can target likely repeat buyers; validated with ROC/PR AUC and cross-validation.
• Automation: Building and maintaining automated data pipelines and reporting workflows using Python and SQL.
• Communication: Communicating technical analysis and model results to teams across finance, operations, and marketing in clear, concise presentations, and training teams across the group to use the dashboards themselves.

Led research into nanoscale heating of domain walls in ferroelectrics within the Centre for Quantum Materials and Technologies (CQMT). Developed expertise in AFM-based characterisation, nanofabrication, and computational analysis of large datasets. Self-motivated and independent, carrying projects end-to-end from initial idea through experiment design and analysis to published conclusions.

• Research: Developed new methods for direct thermal imaging of domain walls, combining AFM techniques with advanced signal processing. Standardised the integration of external hardware into the transmission electron microscope, enabling new research applications.
• Computation: Analysed large datasets including sensitive temperature and electrical measurements using MATLAB, Python (NumPy, pandas), and finite element modelling (COMSOL Multiphysics). Applied regression, signal processing, and image analysis.
• Nanofabrication: Designed and fabricated custom device geometries to solve experimental problems and advance projects investigating nanoscale thermal properties.
• Conferences: Presented research at 7 international conferences across Europe, USA, and Asia. Awarded best poster prize at ECEXII 2023.

Publication: Direct Thermal Imaging of Domain Wall Hot Spots in LiNbO₃ — Lynch et al. Small, Vol. 22, Issue 1, 2025. doi.org/10.1002/smll.202508603

Supervised undergraduate students during practical coding sessions, explaining physics problems and debugging Python scripts. Prepared maths assignment solutions for tutorial sessions and responsible for marking and grading.

Recipient of 9 scholarships and awards based on top-of-year performance, including Seagate Technology Prize for solid-state physics and Karl George Emeleus Physics Prize for distinction in final Honours examinations. Lead peer mentor (2019–2021). MSci project: Tuning the Spatiotemporal Evolution of Dose due to Protons in Water.

3 A*s at A-Level (Physics, Chemistry, Maths).