What we’re looking for

● Robust experience applying AI/ML to healthcare data and clinical problems.

● Hands-on with medical datasets (clinical / EHR, imaging, labs, or registries) and their real-world pitfalls.

● Solid understanding of clinical workflows and how models fit into care.

● Rigorous model validation: study design, metrics, bias, generalization, and clinical safety.

● Discipline around reproducibility, documentation, and data governance.

Technical skill stack

The tools and technologies you should be strong in. We don’t expect every single item — depth in the core stack matters most.

● Programming: Python (expert) and SQL; R a plus

● ML & deep learning: scikit-learn, PyTorch, TensorFlow / Keras, XGBoost / LightGBM

● Data engineering: pandas, NumPy, Polars, SciPy, statsmodels; reproducible pipelines

● Clinical NLP: Hugging Face Transformers, spaCy / scispaCy,



cTAKES or MedCAT for clinical text

● Medical imaging (role-dependent): MONAI, SimpleITK, pydicom, nibabel, OpenCV

● Healthcare data standards: FHIR, HL7, DICOM, OMOP CDM; ICD, SNOMED CT, and LOINC coding

● Statistics & validation: biostatistics, survival analysis, calibration, causal inference, bias / fairness metrics, study design

● MLOps: MLflow, Weights & Biases, DVC, Docker; experiment tracking and model registries

● Data platforms: PostgreSQL, BigQuery or Snowflake; Spark a plus

● Cloud & AI platforms: AWS SageMaker, GCP Vertex AI, or Azure ML

● Privacy & compliance: PHI / HIPAA handling, de-identification, and data governance (GDPR / PIPEDA aware)

📌 Data Scientist (Toronto)
🏢 Barnabus Labs
📍 Toronto