Data Scientist, Advanced Analytics & Commercial Effectiveness
HybridMississauga, Ontario, Canada
Job Summary
Conduct statistical authority and predictive modeling using XGBoost, LightGBM, and survival analysis to optimize commercial strategy and patient outcomes for rare diseases. Build causal impact frameworks, Bayesian MMM, and next-best-action engines integrated into Veeva CRM to drive field execution and revenue planning. Deploy agent-assisted development with Snowflake Cortex AI while maintaining rigorous validation, guardrails, and HIPAA-compliant data operations. Translate publication-grade analytics into actionable recommendations for Brand, Market Access, and Field teams. Work minimum three days per week in-office with flexible hybrid arrangements.
Required Qualifications
- Master's or PhD in Statistics, Biostatistics, Data Science, Econometrics, Applied Mathematics, or a related quantitative field
- 6+ years in data science, applied statistics, or quantitative commercial analytics with a track record of deploying production-grade models in healthcare or life sciences
- Expert-level proficiency in hypothesis testing, regression analysis (linear, logistic, mixed-effects, regularized), ANOVA, survival analysis, Bayesian inference, experimental design, power analysis, significance testing, and multiple comparison corrections
- Deep understanding of when statistical methods apply, when they break down, and how to adapt for small-population rare disease contexts
- Proficiency in XGBoost, LightGBM, Random Forest, SVM, ensemble methods, neural networks, and time-series forecasting with thorough validation (cross-validation, precision-recall, ROC/AUC, calibration)
- Expert-level Python (scikit-learn, XGBoost, LightGBM, statsmodels, lifelines, scipy.stats, PyMC, CausalML, DoWhy, SHAP, PyTorch) and SQL
- Proficiency with Jupyter, Git, and CI/CD integration for model deployment
- Proficiency with Snowflake (Snowpark Python, Snowpark Container Services, Cortex AI), Spark/PySpark, and MLflow or equivalent experiment tracking and model registry tools
- Hands-on experience building Bayesian MMM (PyMC, LightweightMMM, Robyn) and Next-Best-Action recommendation engines for pharmaceutical promotional optimization
- Experience with AI coding agents (Cortex AI, Claude Code, Copilot) for analytical development
- Ability to critically evaluate agent-generated code and identify incorrect statistical reasoning
- Solid understanding of HIPAA de-identification standards, model explainability frameworks (SHAP, LIME), bias detection, and compliance with regulated healthcare data environments
- Ability to translate sophisticated statistical findings into actionable recommendations for non-technical commercial stakeholders and senior leadership
- Minimum of three days per week from the office
Desired Qualifications
- Experience in rare disease or specialty pharma analytics — small-population modeling, patient identification, specialty pharmacy data, hub/PSP, REMS-related data, and high-value-per-patient environments
- Hands-on experience with Komodo Health (open and closed claims), IQVIA (Symphony, NPA, DDD), Veeva CRM, MMIT, Model N, specialty pharmacy dispense data, and EMR/EHR data
- Experience with NLP (topic modeling, NER, embeddings, text classification) and neural network architectures (RNNs, LSTMs, transformers) for healthcare analytics applications
- Experience with RLHF concepts, benchmark design, systematic prompt evaluation, and agent reasoning quality assessment
- Proficiency with PowerBI, Tableau, or Qlik for executive-facing dashboards and self-service reporting
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