Experience on

Research Assistant (Biophysics)

Thu, 01 May 2025 00:00:00 +0100

Designed a framework to read a variety of structured and unstructured protein databases using Python, Pyspark (from Apache Spark) to enable us to make side-by-side comparisons of how other labs study how proteins move, and thereby test the effectiveness of our own methods.
Used framework to pipeline protein data where it was queried in Spark-SQL, enabling rapid performance on large datasets.
Ensured code quality with pre-commit, black, isort, flake8 and Github Actions
Wrote Python libraries for serializing/deserializing PDB files and transforming protein data as Pandas Dataframes, enabling other teams to work with protein data in an analytics-friendly format (such as Apache Parquet).
Presented research finding to department faculty in the form of a public lecture.

Sun, 01 Oct 2023 00:00:00 +0100

Built and Maintained data pipelines in Azure Databricks, and Unity Catalog and data modeling principles to transform raw data from clinical trials (collected through our software) into easily accessed data marts and reports that enable our clients to make effective decisions about ongoing drug trial design.
Developed and orchestrated the above pipelines with Airflow and dbt for large-scale client datasets, using templated SQL and incremental loading strategies to improve data delivery timeliness, reliability, and traceability.
Investigated and corrected source-code defects for clinical data-entry software written in C# and Microsoft SQL (tSQL), and wrote performance-optimized queries to safely fix any bad data generated through bugs or through user error.
Debug codebase software in C# and tSQL to identify and fix problems in product
Automated development environment setup using Docker, Git, and GitHub CI/CD workflows, improving deployment consistency and reducing onboarding and integration time across teams.

Sat, 01 Sep 2018 00:00:00 +0100

Led development of a centralized data warehouse and performance-optimized SQL reporting workflows, improving data accessibility and reducing report generation time for business operations.
Gathered business requirements and designed operational reports used daily by management teams to prioritize workloads and support decision-making.
Collaborated with technical and non-technical stakeholders to translate operational needs into reporting and data solutions, improving adoption and cross-team communication.
Analyzed structured and unstructured lead generation data to identify high-conversion contact patterns, improving targeting efficiency and optimizing outbound call campaigns.
Built and maintained REST API data ingestion pipelines to integrate new lead sources, expanding available business intelligence data and improving pipeline reliability.