Senior Data Scientist
1 day ago
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.
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