Senior Data Scientist

Job Description

Key Responsibilities Design, build, and deploy end-to-end machine learning solutions across structured (tabular) medical datasets and unstructured/non-tabular data (text, images, OCR, signals). Apply advanced ML, deep learning, and LLM-based approaches to solve real-world healthcare/medical challenges. Collaborate with cross-functional teams (engineering, product, domain experts) to translate business requirements into scalable data science solutions. Drive MLOps best practices: experiment tracking, model versioning, CI/CD, monitoring, and automated retraining pipelines. Ensure production-grade robustness using Test-Driven Development (TDD) and OOP principles in ML pipelines. Contribute to architecture discussions and mentor junior team members. Technical Skills & Requirements Strong proficiency in Machine Learning, Deep Learning, and Statistical Modeling. Expertise in handling tabular medical data (EHRs, lab reports, prescriptions) and non-tabular data (text, imaging, audio). Experience with LLMs (fine-tuning, prompting, integration with downstream tasks). Hands-on experience with MLOps frameworks and practices : MLflow, DVC, Airflow, Kubeflow, Weights & Biases Model deployment and monitoring in production Strong software engineering skills: Python, SQL, TDD, OOP, design patterns. Containerization & orchestration: Docker, Kubernetes (nice-to-have) . Familiarity with cloud platforms ( AWS / GCP / Azure ) for scalable ML solutions. Knowledge of medical data compliance standards (HIPAA, GDPR) is a plus. Preferred Frameworks/Tools ML/Deep Learning: scikit-learn, PyTorch, TensorFlow, Hugging Face, XGBoost, LightGBM NLP/LLMs: Transformers, LangChain, RAG pipelines MLOps: MLflow, DVC, Airflow, Kubeflow Data Processing: pandas, PySpark, SQL, Polars Soft Skills Strong problem-solving and analytical mindset. Ability to explain technical concepts to non-technical stakeholders. Leadership and mentorship capabilities.