I took an end-to-end workflow that was inconsistent and prone to failure, analysed where it broke down in practice, and redesigned it so it produced more reliable and repeatable results.
Mapped the workflow to identify fragile steps, operator-dependent judgement points, and poorly constrained decisions.
Made key steps explicit and repeatable, reducing dependence on informal judgement.
Tested revisions using available data to confirm improved robustness and reduced failure.
I replaced inconsistent, subjective decision-making with a clear, evidence-based set of rules that made outcomes consistent, transparent, and defensible.
Converted implicit expert judgement into documented decision rules.
Assessed how rule choices affected downstream outcomes (bias, scatter, discrimination) using benchmark datasets.
Improved auditability and comparability across outputs generated by different people and sources.
3) Reliability improvement and risk reduction (alternative method evaluation)
I identified a recurring source of failure in a workflow and introduced an alternative approach that reduced risk and improved reliability without adding unnecessary complexity.
Diagnosed when and why failures occurred and what conditions triggered them.
Compared alternative approaches to reduce failure modes while managing trade-offs.
Documented where the alternative improved reliability and where limitations remained.
4) Quality assurance through independent cross-checks
I built in independent cross-checks so results were validated early, reducing the risk of unreliable outputs affecting later decisions.
Used cross-method comparison as a structured QA gate (early in the workflow, not at the end).
Flagged outputs likely affected by method-specific artefacts before they propagated downstream.
Reduced the probability of spurious conclusions driven by a single approach.
5) Building and reconstructing a large dataset (data integration)
I consolidated and quality-checked large datasets, expanded them through literature review, and rebuilt them consistently over time to support reliable long-term analysis.
Integrated heterogeneous sources into a coherent, standardised dataset.
Applied consistent assumptions and documented transformations for reproducibility.
Explicitly tracked limitations (coverage gaps, uneven quality, potential bias) and incorporated them into interpretation.