Hi, my name is

Lauren Brixey

I am a

Data Scientist

Harnessing a scientific background to build data-driven solutions
View my resume

About Me

My journey began in science, where I spent over four years as a process chemist designing experiments and developing routes to deliver pharmaceutical drugs for clinical trials. Along the way, I discovered the power of data science to uncover patterns and drive better decisions, which led me to complete certifications from the University of Cambridge and Google.

Since then, I’ve applied machine learning, deep learning, and time series forecasting to projects spanning predicting clinical trials dropouts to an NLP challenge with the Bank of England. By combining scientific rigour with domain expertise in chemistry and pharmaceuticals, I focus on building interpretable, data-driven solutions that deliver real impact in healthcare, life sciences and beyond.

At the heart of my work is curiosity. I believe the quality of insights is limited only by the quality of the questions you ask and it’s this mindset that drives me to explore data with rigour and insight.

Here are a few Python libraries I’ve been working with recently:

  • Machine Learning

    pandas, NumPy, scikit-learn, SHAP

  • Deep Learning

    TensorFlow, Keras, PyTorch

  • NLP

    spaCy, NLTK, HuggingFace transformers

  • Time Series

    statsmodels, pmdarima, sktime, LightGBM, XGBoost

  • Data Visualisation

    matplotlib, seaborn

Image of Lauren Brixey

Projects

Natural Language Processing

Uncovering Risk in Bank Earnings Calls (Bank of England)

GitHub

Worked in collaboration with the Bank of England using NLP to detect early warning indicators of risk in Q&A transcripts

  • Hugging Face transformers
  • scikit-learn
  • scipy
  • sentence-transformers

Applying NLP for Topic Modelling in Fitness Industry

GitHub

Extracted key drivers of customer dissatisfaction to reduce churn risk using sentiment-aware topic modeling and emotion detection on gym reviews.

  • BERTopic
  • LDA
  • LLMs

Classification

Hypertension Clinical Trial Dropout Prediction (ongoing)

GitHub

Predicted dropout and adverse event risk in Phase III clinical trial data (~1,000 patients across 50+ sites) using recall-optimized Gradient Boosting classifiers.

  • XGBoost
  • Logistic Regression
  • SHAP

Predicting Student Dropout

GitHub

Built a machine learning model to predict student dropouts, prioritising recall to identify at-risk students early for timely intervention.

  • scikit-learn
  • XGBoost
  • Keras
  • TensorFlow

Time Series Forecasting

Using Time-Series Analysis for Sales and Demand Forecasting

GitHub

Enabled proactive inventory planning by forecasting weekly and monthly Book demand.

  • ARIMA
  • XGBoost
  • LSTM

Clustering

Customer Segmentation for Retail Marketing

GitHub

Applied unsupervised learning to segment customers by behaviour and spending patterns, enabling targeted marketing and retention strategies.

  • K-Means
  • Agglomerative Clustering
  • PCA
  • t-SNE

Anomaly Detection

Detecting Anomalous Ship Engine Activity

GitHub

Developed an anomaly detection system to identify potential ship engine failures early, enabling proactive maintenance and improved operational efficiency

  • OneClassSVM
  • Isolation Forest
  • PCA

Education

Data Science with Machine Learning & AI

Studied a Masters Level Qualification' at University of Cambridge.

January, 2025 - October, 2025

Chemistry with Biological and Medicinal Chemistry

Studied a Master of Chemistry at University of York.

September, 2016 - May, 2020

Experience

Pharmaron

Senior Scientist I (July, 2024 - January, 2025)
Scientist II (April, 2022 - June, 2024)
Scientist I (August, 2020 - March, 2022)

Experimental Design: Designed experiments and scaled processes to deliver pharmaceutical drugs for clinical trials.
Process Modelling: Applied data-driven modelling to simulate chemical processes, optimise performance, and ensure safety.
Scientific Communication: Collaborated with stakeholders and clients to translate complex data into clear, actionable insights.

Get In Touch

My inbox is always open. Let’s connect about data science opportunities in healthcare and beyond!

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