Description

Organised and detail-driven Data Scientist/Analyst with experience managing large datasets and creating clear reports that support better decision-making. Skilled in using tools such as SQL, Power BI, Excel, Python, and R to keep data accurate, easy to understand, and useful for staff at all levels. In previous roles, I have improved reporting processes, reduced manual work, and built dashboards that made information more accessible to managers and teams. Known for working closely with colleagues, explaining insights in simple terms, and ensuring that data is used effectively to achieve organisational goals.

Languages

English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Spashisha Ltd
Junior Data Analyst
October 2023 - September 2024 (11 months)
Manchester, UK
• Engineered and maintained Power BI dashboards, which were embraced by cross-functional teams, enhancing reporting efficiency by 30%.
• Formulated predictive models using R and Python, accurately forecasting key business metrics with up to 85% precision.
• Collaborated with stakeholders to interpret business requirements, devising data solutions that curtailed reporting turnaround time by 25%.
• Executed exploratory data analysis on healthcare datasets, pinpointing operational bottlenecks and providing recommendations that optimised process efficiency.
Global Payments,
Junior Data Analyst
June 2021 - July 2022 (1 year and 1 month)
London, UK
• Oversaw and queried a 15TB SQL database, enhancing query efficiency and streamlining reporting workflows.
• Designed interactive dashboards in Power BI employed by senior managers to guide strategic planning, decreasing manual reporting by 40%.
• Performed anomaly detection and trend analysis, improving the precision of financial reporting and risk assessment.
◦ Partnered with senior analysts to refine methodologies, boosting reporting accuracy across various teams.
Maywood Ltd NHS/ University of Greater Manchester
Data Analyst (Industry Project)
June 2025 - September 2025 (3 months)
Bolton, VT, USA
• Scrutinised over 23 NHS booking datasets using SQL and Python, amalgamating 24 fragmented sources into a cohesive dataset.
• Devised a utilisation KPI which uncovered an average usage rate of 20.8% spanning 333 rooms.
• Executed concurrency analysis using a sweep-line algorithm, identifying peak demand levels of 3,658 simultaneous bookings.
• Crafted a simulation model that attained 80-100% reassignment success rates, aiding in closure/repurposing decisions without service disruption.
• Developed dashboards and statistical visualisations, culminating in policy recommendations for NHS estate optimisation.