When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Are you ready to bring publication-grade statistical difficulty and AI-native analytics together to uncover hidden patients, elevate commercial strategy, and improve outcomes for people living with rare diseases? In this role, you will turn sophisticated, de‑identified healthcare data into decisions that sharpen field execution, optimize investment, and ultimately reach those who need therapies most. You will join a fast-moving analytics team that pairs deep statistical expertise with modern machine learning and Snowflake Cortex AI to accelerate model development without compromising interpretability or compliance. From patient identification and adherence prediction to marketing effectiveness and causal impact, you will deliver models that are trusted by leaders and used by teams every day. Statistical Authority: Act as the go-to expert to set analytical standards across hypothesis testing, regression, inference, and experimental design; Predictive Modeling: Design, validate, and deploy models using XGBoost, LightGBM, Random Forest, SVM, neural networks, and ensembles; address class imbalance with robust evaluation and calibration to drive exact commercial actions. Build survival models (Cox, AFT, competing risks) to predict adherence, discontinuation, and patient lifetime value that inform proactive interventions. Create and maintain ensemble time-series frameworks (ARIMA, Prophet, exponential smoothing, gradient-boosted) to guide demand planning, revenue scenarios,



and launch-readiness decisions. Causal Impact: Design A/B tests and apply quasi-experimental methods (DiD, PSM, synthetic control, IV, RDD) to quantify the true effect of commercial initiatives on prescribing and patient outcomes. Marketing Mix Optimization: recommend promotional reallocations that improve impact across personal and non-personal channels. integrate daily actions into Veeva CRM. Train supervised classifiers on claims, labs, and specialty pharmacy data to prioritize likely undiagnosed patients and direct field resources where they matter most. Deploy models that detect early risk signals from dispense intervals, hub interactions, and scheduling patterns to reduce discontinuation. Competitive Intelligence: Create real-time switching surveillance models from claims and formulary data to anticipate market dynamics and inform agile responses. Agent-Assisted Development: Use Snowflake Cortex AI and AI coding agents to speed prototyping and feature engineering while retaining full human-led statistical validation. Build guardrails and evaluation suites that stress-test agent outputs, catch plausible-but-wrong reasoning, and prevent flawed insights from reaching decisions. Agent Tuning and Evaluation: Design domain-specific prompts, benchmarks, and feedback loops to continuously improve agent analytical performance. HIPAA-Compliant Data Operations:



Work exclusively with de-identified patient-level data; implement minimum-necessary access and maintain re-identification risk assessments. Implement bias detection, fairness audits, SHAP/LIME, drift monitoring, and validation gates; maintain end-to-end audit trails aligned to FDA, REMS, GDPR, SOC2, 21 CFR Part 11, and enterprise AI governance. Translate sophisticated statistics into clear recommendations for Brand, Market Access, Patient Services, and Field teams; influence senior leaders with evidence that drives action. Documentation and Enablement: Create meticulous documentation, code reviews, and trainings that set the standard across the analytics community and ensure sustainable, repeatable excellence. Master’s or PhD in Statistics, Biostatistics, Data Science, Econometrics, Applied Mathematics, or a related quantitative field.

Experience:
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. Predictive Analytics & ML: 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). Programming & Libraries: 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 dep

📌 Data Scientist, Advanced Analytics (Mississauga)
🏢 AstraZeneca
📍 Mississauga