Laura Alonso

Designed and implemented BigQuery/dbt data pipelines consolidating order systems into analyst-ready models. Built real-time cancellation tracking as the source of truth, reducing shipment errors and financial losses. Defined NPS KPI logic across customer touchpoints and modelled reverse logistics data to analyse return profitability. Supported Supply, CX, Omnichannel and Marketing teams with SQL models, reports and dashboards. Tech: BigQuery, SQL, dbt, Looker Studio, Power BI, GitHub.

Designed BigQuery data architecture for a fintech loan platform from scratch, covering ingestion, ETL/ELT, deduplication, data quality checks and production datamarts. Optimised pipeline performance by separating high-frequency control events, reducing query costs and improving speed. Built schema-drift checks, ML models for loan applicant segmentation and sales forecasting, plus clickstream analysis to identify UX friction. Tech: BigQuery, SQL, Dataform, Python, R.

Statistical analysis of large-scale educational datasets including PISA, psychometric instrument design and validation, and machine learning modelling for educational research. Stack: R, SPSS, Excel.

• Core areas: Big Data, Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), Recommender Systems, Time Series Analysis, Graph-based Systems. • Technologies: Python, R, Hadoop, Spark, MongoDB, Cassandra, Google Cloud Platform (GCP), AWS, Azure. • Thesis: Hybrid recommender system integrating content-based filtering, NLP embeddings, and graph-based collaborative filtering — built using Steam API data. Available on GitHub upon request.

• Specialised in applied statistics, predictive modelling, multivariate analysis, regression, clustering, meta-analysis, and epidemiological methods. • Thesis: Factor structure and differential item functioning of the MMSE — UAM Faculty of Medicine, Project COURAGE in Europe.

• Clinical internship at Centro Hermanas Hospitalarias (psychiatric comorbidities). • Thesis: psychological variables associated with therapeutic adherence in COPD patients.