Data Portfolio

Analytics & Data Science
Projects

Senior Production and Development Management professional with 15 years of industry experience across the Video Game and Defense sectors. M.Sc. in Data Science, B.Sc. in Statistics. This portfolio showcases applied work in data analytics, predictive modelling, and interactive reporting across gaming, localization, and labour market domains.

Power BI Streamlit Python XGBoost Snowflake Madrid, ES
Power BI 01 / 04

Data Science Salaries — Global Analysis

An exploratory analysis of compensation trends across Data Science and adjacent roles, built as part of an M.Sc. programme. Covers salary distributions by job title, experience level, company size, remote ratio, and geography using publicly available survey data. Includes geo-located KPI views and cross-filtering across multiple dimensions.

Power BI DAX Labour Market Data Geo Visualisation M.Sc. Project
Power BI 02 / 04

Vendor Management Dashboard

An operational analytics dashboard demonstrating how internal form-based feedback can be integrated with vendor performance data to support structured supplier evaluation. Designed around a realistic localization vendor management workflow, combining quantitative KPIs with qualitative scoring from internal stakeholder submissions.

Power BI DAX Vendor Management Operational Analytics Forms Integration
Streamlit 03 / 04

Voice Over Session Cost Predictor

An end-to-end machine learning system for predicting Voice Over localisation session costs. Built on a synthetic dataset modelling realistic VO production parameters, an XGBoost regression model achieves 13.9% MAPE (Stage 1) and 23.7% MAPE (Stage 2), against baselines of 19.2% and 28.2% respectively. Includes SHAP-based explainability and a Streamlit interface for interactive quote generation.

Python XGBoost SHAP Streamlit Localization Predictive Modelling
Streamlit 04 / 04

Veilstone — Gaming Analytics Report

A full-stack analytics report for a fictional action-exploration title, demonstrating applied data science in a live-service gaming context. Covers cohort retention analysis, A/B test evaluation, player segmentation, difficulty curve analysis, and additional behavioural metrics — all built on a synthetic but structurally realistic dataset.

Python Streamlit Cohort Analysis A/B Testing Player Segmentation Gaming Analytics