George Vlad

I designed and implemented a high-performance Python engine for large-scale survey data aggregation in a global market research environment. The system replaced slower, memory-heavy processing flows with a sparse matrix-based architecture capable of handling complex questionnaire structures and multiple data representations.

I rearchitected the computation layer around CSR/CSC matrices using NumPy and SciPy, enabling vectorized cross-tabulation, weighted aggregation, and statistical calculations without materializing large intermediate datasets. Aggregations were performed directly on encoded matrices, reducing data movement and avoiding dense expansions.

The engine improved processing time from several hours to seconds while significantly lowering memory usage. I also implemented encoding, indexing, and alignment logic to preserve links to the original dataset and produce deterministic, reproducible outputs.

I worked closely with analytical stakeholders to validate statistical correctness and integrate business rules. The design also created a scalable foundation for significance testing, diagnostics, and future statistical extensions.

I designed and implemented diagnostic and documentation tooling for a statistical testing system used in large-scale analytical workflows. The goal was to make complex significance testing logic transparent, reproducible, and easier to validate by exposing the calculations behind each result.

I built tooling that generated detailed diagnostic outputs for pairwise statistical tests, including differences, bases, effective bases, overlaps, standard errors, test statistics, and p-values. This allowed engineers and analytical stakeholders to inspect the underlying numbers instead of treating the statistical engine as a black box.

I also implemented formula documentation views that explained the calculation paths used by the significance testing system, supporting clearer validation, debugging, and communication with non-engineering stakeholders.

The work improved confidence in statistical correctness, made edge-case debugging faster, and created a stronger foundation for maintaining and extending the testing engine over time.

I designed and implemented a statistical significance testing engine for large-scale survey analytics, built on top of a sparse matrix aggregation system. The goal was to support reliable comparison of means and proportions across complex cross-tabulated outputs while preserving performance, correctness, and transparency.

I implemented pairwise statistical testing logic, including support for means, proportions, weighted bases, effective bases, standard errors, t-statistics, p-values, and overlap-aware calculations. The engine was designed to work efficiently with CSR matrices produced by the aggregation layer, avoiding unnecessary dense transformations and keeping memory usage low.

I also handled complex analytical cases, such as grouped outputs, rebased calculations, multi-response variables, and dichotomous set data. The implementation included validation logic, diagnostics, and regression tests to ensure numerical correctness across edge cases.

This work improved the reliability and scalability of statistical testing in production analytical workflows and made the calculation layer easier to inspect, debug, and extend.

I designed and implemented a FastAPI-based analytics service to calculate weekly offered shipping capacity from structured schedule data. The goal was to expose a clean API that could compute capacity trends for a specific trade lane while keeping the business logic transparent and testable.

I implemented the analytical logic primarily in SQL, using SQLite and recursive CTEs to generate weekly periods, normalize dates, deduplicate vessel journey records, and calculate offered capacity per week. I also added a rolling four-week average to provide a smoother view of capacity trends across the requested date range.

The service was built with a simple, maintainable architecture using FastAPI, SQLite, and automated tests. I focused on clear query design, reproducible outputs, and endpoint behavior that could be validated with sample datasets.

This project demonstrated back-end API development, SQL-based analytical processing, and the ability to translate a business problem into a reliable data service.

Project Pulse is a public-preview full-stack SaaS-style application for freelancers and contractors to manage workspaces, projects, tasks, payment records, and business operations from a responsive dashboard. I designed and implemented the FastAPI back end with SQLAlchemy models, Alembic migrations, Pydantic schemas, authentication, account export/deletion, password reset, and email-confirmation flows, admin feedback handling, observability, and integration tests. I also built the React and TypeScript/Vite front end, including auth screens, dashboard metrics, workspace settings, project/task forms, filters, payment history, and API client integration. The project includes CI, back-end test coverage, front-end build validation, and production-oriented configuration guidance.