I am a data analyst with seven years of experience working with complex datasets in environments where accuracy, governance and the reliability of analytical outputs directly affect the quality of decisions made. I have spent that time building the habits that matter most in analytical work: questioning assumptions, validating findings, communicating clearly and taking full ownership of the work from the first question to the final output.

The projects in this portfolio apply that experience to large scale real world datasets using Python, SQL and Power BI.

Seven years of progressive analytical experience across regulated and operationally complex environments. My work has consistently involved large scale data analysis, data quality assessment, root cause investigation, governance documentation and the communication of complex findings to both technical and non-technical audiences.

I have worked in environments where data errors have real consequences, where audit readiness is a daily requirement and where the difference between a finding and a conclusion matters. That experience shapes how I approach every analytical problem regardless of domain or industry.

I am someone who genuinely enjoys the process of getting things right. That enjoyment shows up consistently in what I do as an analyst. I ensure the quality and integrity of my work are sound before I share it. I ask whether my conclusion actually follows from my evidence. I think about who will read my findings and what they need to understand before I decide how to present them. None of this feels like extra effort. It feels like the job.

I care about the person at the end of every analysis I carry out and the impact that analysis will have on the decision they are about to make. That keeps me honest about what I can and cannot claim, careful about how I communicate uncertainty and clear about the difference between what the data shows and what I think it means.

I take ownership quietly. I do not need to be managed toward finishing something or reminded that accuracy matters. Both are reflections of a standard I hold myself to regardless of whether anyone is watching.

I apply the same care to how I handle data. I am mindful of access boundaries, protective of data integrity throughout the analytical process and aware that behind every dataset there are real people whose information has been entrusted to the organisations that hold it.

I have worked through problems alone and contributed to team efforts where collaboration and clear communication produced better outcomes than either person would have reached independently. I know when to ask for input and when to get on with it. I fit into the culture around me rather than waiting for it to fit around me.

Everything I build is serious and deliberate. The projects in this portfolio are evidence of that.

I want to work somewhere that takes its data seriously. That is the environment where I will do my best work and be most useful to the people around me.

Data Analysis and Engineering Extracting, transforming and analysing large datasets using Python and SQL. Building end to end analytical pipelines from raw data sources to dashboard delivery. Identifying patterns, trends and anomalies in complex multi-variable datasets across regulated and operational environments.

Statistical Analysis and Signal Detection Applying statistical signal detection methods to identify anomalies and elevated patterns in large datasets. Cross-validating findings across Python and SQL to ensure analytical integrity. Presenting findings with appropriate uncertainty and analytical hedging.

Data Quality and Governance Assessing, validating and documenting data quality across complex operational datasets. Maintaining audit ready processes and traceable analytical outputs. Applying governance frameworks in regulated and complex data environments.

Data Visualisation and Reporting Building interactive Power BI dashboards for executive and operational audiences. Producing publication quality charts using matplotlib and seaborn. Writing analytical findings in plain language accessible to non-technical stakeholders.

Database and SQL Designing and querying relational databases in PostgreSQL. Writing analytical SQL including window functions, CTEs and aggregations. Loading, transforming and validating data between Python and SQL environments.

The business question: Which drugs in the FDA adverse event database carry the highest safety risk and what specific reactions are statistically elevated?

6,000 adverse event reports retrieved via API across five pharmaceutical products. Applied statistical signal detection methodology to identify drug reaction combinations reported more frequently than expected by chance. Delivered findings across 15 Python analyses, 15 SQL queries and a 3 page Power BI dashboard.

Outcome: Identified Ibuprofen as carrying the highest mortality signal at 20.50% death rate. Detected Drug withdrawal syndrome in Paracetamol combination products at a signal strength of 777, meaning it was reported 777 times more frequently than expected by chance. Confirmed Metformin Lactic acidosis signal consistent with clinical literature.

Transferable value: Signal detection, anomaly identification, regulatory data analysis, API data engineering, executive dashboard delivery.

Stack: Python · PostgreSQL · Power BI · SQL · Jupyter · REST API

The business question: Is the NHS meeting its 18-week constitutional standard and where are the greatest performance pressures?

11 months of national RTT data covering 515 NHS trusts analysed across 23 treatment specialties. Delivered findings across 15 Python analyses, 10 SQL queries and a 2 page Power BI dashboard.

Outcome: NHS missed the 92% standard in every reporting period. Waiting list reduced from 7.42 million to 7.16 million patients. Oral Surgery identified as worst performing specialty at 51.5%.

Transferable value: Performance benchmarking, KPI monitoring, trend analysis, operational reporting, large dataset handling.

Stack: Python · PostgreSQL · Power BI · SQL · Jupyter · pandas

The business question: What is the scale of A&E performance failure and how does winter pressure affect emergency care capacity?

Full year A&E data across 200 NHS providers analysed across 8 dimensions including seasonal variation, provider benchmarking and regional comparison.

Outcome: 26.9 million attendances recorded. 4-hour breach rate 39.4%, nearly double the NHS target. 570,931 patients waited 12 or more hours before being admitted to hospital.

Transferable value: Seasonal analysis, provider benchmarking, capacity planning, operational performance reporting.

Stack: Python · Jupyter · pandas · matplotlib · seaborn

The business question: Can enterprise authentication attacks be reliably detected using Windows Security event logging?

Production-modelled Active Directory environment built on Windows Server 2022. Simulated brute-force, privilege escalation and authentication abuse scenarios. Validated detection across 9 Windows Security event captures.

Outcome: Successfully detected all simulated attack scenarios. Validated detection coverage across authentication, privilege and process execution event categories.

Transferable value: Audit log analysis, structured event data validation, pattern detection in large log datasets, enterprise infrastructure understanding, data security awareness.

Stack: Windows Server 2022 · Active Directory · PowerShell · VirtualBox

I approach every analysis the same way regardless of domain or industry.

Start with the business question: What decision does this analysis need to support?

Understand the data: Assess completeness, quality and limitations before drawing conclusions.

Apply appropriate methodology: Match the analytical approach to the question being asked.

Validate findings: Cross-check results across different tools and approaches.

Communicate clearly: Present findings in language a non-technical stakeholder can act on.

This workflow has been applied consistently across seven years of professional analytical work and across every project in this portfolio.

• Google Cybersecurity Certificate
• CompTIA Security Plus

Both inform my understanding of data security principles and responsible data handling in analytical environments.

• Clinical Trial Analysis using ClinicalTrials.gov data

Data analyst roles in environments where analytical rigour, data governance and clear communication of findings are valued. I bring seven years of professional analytical experience alongside a portfolio of end to end projects built on real world government and regulatory data.

Based in the United Kingdom. Open to remote and global opportunities.

Updated regularly as new projects are completed.