Senior Data Scientist

Data Scientist (8+ years) working at the intersection of statistical genetics, population genomics, and applied machine learning in biomedical research. MEng in Big Data Analytics, PhD in Computational Physics.

Population Genomics & Statistical Genetics

• GWAS, polygenic risk scores (PRS), multi-ancestry analysis
• PRS tools: SBayesR (via GCTB), LDpred2, PRS-CS, PLINK (--score)
• Causal inference: Mendelian randomization-style approaches using pharmacogenomic variants (e.g. CYP2D6, CYP2C19, CES1) as genetic instruments
• Martingale residual transformation for Cox-to-linear GWAS
• Large-scale cohort analysis (e.g. iPSYCH2015, N=105,477; ~10,000 PGS computed from FinnGen/UK Biobank summary statistics)

Machine Learning & Predictive Modeling

• Programming: Python, R, SQL, Bash
• Statistical modeling: generalized linear models, multivariate regression, time-series analysis (scikit-learn, statsmodels, pandas, numpy)
• Predictive modeling: LASSO, Random Forest, XGBoost, ensemble/Super Learner methods — applied to clinical prediction modeling in oncology trial data
• Applied transformer models via the Hugging Face pipeline API (BERT, DistilBERT, Sentence-BERT, BERTopic) and PyTorch for GPU-accelerated inference on HPC systems — applying pretrained models, not training architectures from scratch

Infrastructure & Pipelines

• HPC cluster pipelines, Snakemake (basic level)
• Containerization: Docker, Singularity
• Version control: Git
• Data visualization: Matplotlib, Seaborn, ggplot2

Research & Project Experience

• Phenotype QC, ancestry-stratified association testing, and PRS analysis across large genomic cohorts
• Predictive modeling for clinical trial data, contributing to early-phase trial insight generation
• Cross-institutional collaboration on pharmacogenomic causal inference projects

Soft Skills

• Written and verbal communication in English
• Collaboration in interdisciplinary, multicultural, and cross-institutional teams
• Independent project management