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Elena Vasquez

Senior Data Scientist · 6+ years

Profile

Senior data scientist with 6+ years shipping production ML systems across fintech, e-commerce and healthtech. Deep expertise in experimentation, causal inference and NLP/LLM applications. Hands-on in Python and SQL daily, owning projects end-to-end from problem framing through deployment and monitoring. $8M+ incremental revenue attributed to ML work across the last two roles.

Experience

Senior Data ScientistJan 2022 — Present

RevolutLondon, UK

Global fintech, 40M+ customers, $33B valuation

Own the fraud detection model stack serving 40M+ users: redesigned the real-time scoring pipeline (XGBoost + neural embeddings), reducing false positive rate by 34% while maintaining 99.2% recall
Built an LLM-powered transaction categorisation system using fine-tuned GPT-3.5 + RAG, replacing a rule-based engine and improving accuracy from 71% to 93% across 200+ merchant categories
Designed and ran 50+ A/B tests on premium subscription features; CUPED-adjusted analyses drove $5.2M incremental ARR through paywall and onboarding optimisations
Built the team's experimentation framework — sample size calculators, guardrail metrics, auto-reporting in Looker — adopted by 4 other product squads

Data ScientistMar 2019 — Dec 2021

ASOSLondon, UK

Online fashion retailer, 26M+ active customers, £3.9B revenue

Developed a customer lifetime value model deployed into CRM decisioning, enabling £2.8M in annual savings by shifting acquisition spend toward high-LTV segments
Built a real-time product recommendation engine (collaborative filtering + transformer embeddings) serving 10M+ daily sessions, lifting click-through rate by 19%
Led causal analysis of a returns policy change using difference-in-differences on 18 months of order data, quantifying a 6pp margin improvement that justified the rollout

Junior Data ScientistSep 2017 — Feb 2019

Babylon HealthLondon, UK

AI-powered healthtech, 2M+ registered users

Built NLP classifiers for symptom triage (BERT fine-tuning on 500K labelled consultations), achieving 89% top-3 accuracy and reducing clinician workload by 22%
Designed the analytics layer for the clinical trials dashboard: SQL pipelines, dbt models and Tableau reporting used by the medical affairs team

Education

MSc Machine Learning, Distinction — Imperial College London (2016 – 2017)
BSc Mathematics & Statistics, First Class — University of Barcelona (2012 – 2016)

Publications & Speaking

"Reducing false positives in real-time fraud scoring with hybrid embeddings" — Towards Data Science (2024, 12K+ reads)
Speaker, PyData London 2023 — "Production causal inference: what textbooks don't tell you" (400+ attendees)

pretty · non-ats

Elena Vasquez

Profile

Senior data scientist with 6+ years shipping production ML systems across fintech, e-commerce, and healthtech. Deep expertise in experimentation, causal inference, and NLP/LLM applications. Hands-on in Python and SQL daily, comfortable owning projects end-to-end from problem framing through deployment and monitoring. Track record of translating complex models into measurable business outcomes: $8M+ incremental revenue attributed to ML work across last two roles.

Experience

Senior Data ScientistJan 2022 -- Present

RevolutLondon, UK

Global fintech, 40M+ customers, $33B valuation

Own the fraud detection model stack serving 40M+ users: redesigned the real-time scoring pipeline (XGBoost + neural embeddings), reducing false positive rate by 34% while maintaining 99.2% recall
Built an LLM-powered transaction categorisation system using fine-tuned GPT-3.5 + RAG, replacing a rule-based engine and improving accuracy from 71% to 93% across 200+ merchant categories
Designed and ran 50+ A/B tests on premium subscription features; CUPED-adjusted analyses drove $5.2M incremental ARR through paywall and onboarding optimisations
Built the team's experimentation framework: sample size calculators, guardrail metrics, auto-reporting dashboards in Looker — adopted by 4 other product squads

Data ScientistMar 2019 -- Dec 2021

ASOSLondon, UK

Online fashion retailer, 26M+ active customers, £3.9B revenue

Developed a customer lifetime value model deployed into CRM decisioning, enabling £2.8M in annual savings by shifting acquisition spend toward high-LTV segments
Built a real-time product recommendation engine (collaborative filtering + transformer embeddings) serving 10M+ daily sessions, lifting click-through rate by 19%
Led causal analysis of a returns policy change using difference-in-differences on 18 months of order data, quantifying a 6pp margin improvement

Junior Data ScientistSep 2017 -- Feb 2019

Babylon HealthLondon, UK

AI-powered healthtech, 2M+ registered users

Built NLP classifiers for symptom triage (BERT fine-tuning on 500K labelled consultations), achieving 89% top-3 accuracy and reducing clinician workload by 22%
Designed the analytics layer for the clinical trials dashboard: SQL pipelines, dbt models, Tableau reporting

Skills

ML & modelling: XGBoost, neural networks (PyTorch), NLP/LLMs (fine-tuning, RAG, embeddings), causal inference, time-series, recommendation systems
Experimentation: A/B testing, CUPED, uplift modelling, difference-in-differences, Bayesian methods, sample size design
Engineering: Python (pandas, scikit-learn, PyTorch), SQL (advanced), dbt, Airflow, Spark, Docker, Git, CI/CD, AWS (S3, SageMaker, Redshift)
Visualisation: Looker, Tableau, Streamlit, Matplotlib

Education

Imperial College LondonLondon, UK

MSc Machine Learning, Distinction2016 -- 2017

University of BarcelonaBarcelona, Spain

BSc Mathematics and Statistics, First Class2012 -- 2016

Publications & Speaking

"Reducing false positives in real-time fraud scoring with hybrid embeddings" — Towards Data Science (2024, 12K+ reads)
Speaker, PyData London 2023 — "Production causal inference: what textbooks don't tell you" (400+ attendees)

.pdf · ats-ready

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