● Spearheaded backend process orchestration, streamlining data operations that enhanced integration across 10 client projects and improved internal workflows for a significant boost in productivity.
● Executed comprehensive data cleansing strategies, converting datasets comprising of more than 1 million entries into valuable insights used by project managers to improve strategic decisions on key client engagements.
● Streamlined the management of data flow processes, enabling seamless access to critical datasets for 10+ cross functional teams, ultimately improving collaborative project outcomes and reducing information retrieval times by approximately 30 hours monthly.
● Designed and deployed robust data pipelines utilising SQL and Python, resulting in a 20% enhancement in both data transfer efficiency and processing time for critical datasets.
● Implemented effective strategies for optimizing existing datasets used within analytics frameworks through rigorous cleaning methodologies, resulting in an impressive improvement rate measured at 25% increase in overall accuracy levels observed post-implementation.
● Revamped existing workflows for dataset handling by introducing improved checks and balances, boosting operational efficiency and reducing error margins in monthly analyses.
● Built and deployed a decision tree model to classify individuals in the UK into specific categories for a financial vulnerability segmentation model. Leveraged Python and scikit learn to analyse demographic and financial data.
● Played a key role in the migration of data systems to the cloud, which resulted in better scalability and a 15% cost reduction.
