Data Scientist | 3+ Years of Experience | Predictive Data Analytics, Python, SQL, Machine Learning

I am a results-driven data professional with 3 years of experience as a research analyst and a strong foundation in data analytics, neuroscience, and writing. I specialize in statistical analysis (linear mixed-effects models, survival analysis) and machine learning (SVM, random forests, XGBoost, CatBoost, neural networks) to uncover insights that support strategic decision-making. I enjoy tackling complex problems and collaborating with cross-functional teams to deliver impactful, data-driven solutions.

My core expertise includes R, Python, Pandas, Scikit-learn, and PyTorch for data analysis, statistical modeling, and machine learning. I have experience leveraging GitHub, Jupyter, Docker, and AWS EC2 to develop, manage, and deploy reproducible, scalable data science workflows.

Key Achievements

• Engineered scalable data pipelines to retrieve and integrate large-scale neuroimaging datasets from AWS, enabling efficient machine learning and analytics workflows.
• Partnered with multidisciplinary research teams to design statistical analyses and predictive modeling approaches, translating complex neuroimaging data into actionable research insights.
• Developed and evaluated machine learning (Random Forest, Support Vector Machine, Gradient Boosting) and deep learning (Multi-Layer Perceptron, Convolutional Neural Networks) models to predict brain age from neuroimaging data and investigate associations with mental illness.
• Mentored junior researchers in data science and machine learning best practices while communicating technical findings through reports, presentations, and scientific conferences.

I'm currently seeking a data role where I can apply advanced analytics to solve complex, real-world problems. I'm especially interested in healthcare and technology organizations that leverage data and AI to drive innovation, improve decision-making, and create meaningful impact.