Technical Skills

Selected capabilities relevant to ML researcher and ML engineer roles in biotech, pharma, and translational AI teams.

Modeling Multimodal prediction, survival analysis, uncertainty-aware clinical ML
Systems LLM workflows, reproducible pipelines, data and model versioning
Domain Oncology, transplant medicine, real-world clinical and omics data

⚙ ML Frameworks & Modeling

PyTorch PyTorch Lightning scikit-learn XGBoost LightGBM HuggingFace MONAI Survival Analysis Multimodal Modeling Uncertainty Quantification Model Interpretability

⚡ LLM & Decision-Support Systems

LangGraph RAG Pipelines Multi-Agent Orchestration Prompt Engineering Tool-Use Agents Knowledge Graphs Clinical NLP Clinical Decision Support

💻 Programming & Data

Python NumPy SciPy Pandas Shell / Bash matplotlib seaborn plotly SHAP Data Curation Feature Engineering

🛠 Infrastructure & MLOps

Git DVC Docker CI/CD Cursor Experiment Tracking Reproducible Pipelines Model Versioning

🧬 Biomedical & Pharma-Relevant Domain Expertise

EHR / Clinical Time Series Medical Imaging (CT, Ultrasound) Proteomics & Genomics Transcriptomics (scanpy) Transplant Medicine ICU Prediction Oncology Biomarker Discovery Patient Stratification Real-World Evidence Treatment Response Modeling

👥 Cross-Functional Delivery

Project Leadership Stakeholder Alignment Study Design Scientific Writing Peer Review Mentoring Presentation Clinical Collaboration
Chinese — Native • English — Proficient • German — Basic • Japanese — Basic