Data Scientist, Advanced Analytics & Commercial Effectiveness Full-time

Analytics Role at Alexion, AstraZeneca Rare Disease

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform to connect, work at pace and challenge perceptions. 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. Join us in our unique and ambitious world.

Introduction to Role:

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. Do you thrive at the intersection of analytical depth and real-world impact?

Accountabilities:

• Statistical Authority: Act as the go-to expert to set analytical standards across hypothesis testing, regression, inference, and experimental design; ensure outputs meet publication-grade rigor with clear assumptions, diagnostics, and power.

• 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 precise commercial actions.

• Time-to-Event Analytics: Build survival models (Cox, AFT, competing risks) to predict adherence, discontinuation, and patient lifetime value that inform proactive interventions.

• Forecasting: 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: Develop Bayesian MMM to estimate channel-level return on investment and response curves; recommend promotional reallocations that improve impact across personal and non-personal channels.

• Next-Best-Action Engines: Build and refine HCP-level recommendation systems using contextual bandits, collaborative filtering, and reinforcement learning; integrate daily actions into Veeva CRM.

• Patient Identification: Train supervised classifiers on claims, labs, and specialty pharmacy data to prioritize likely undiagnosed patients and direct field resources where they matter most.

• Adherence and Retention: Deploy models that detect early risk signals from dispense intervals, hub interactions, and scheduling patterns to reduce discontinuation.

• Segmentation and Targeting: Construct HCP and patient segments using clustering and NLP-enriched profiles to focus engagement and tailor messaging.

• 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.

• Validation and Hallucination Detection: 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.

• Model Guardrails and Interpretability: 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.

• Cross-Functional Partnership: 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.

Essential Skills/Experience:

• Education: Master's or PhD in Statistics, Biostatistics, Data Science, Econometrics, Applied Mathematics, or a related quantitative field.

• Experience: 4–8+ years in data science, applied statistics, or quantitative commercial analytics with a track record of deploying production-grade models in healthcare or life sciences.

• Statistical Expertise: 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.

• 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 deployment.

• Data Platform: Proficiency with Snowflake (Snowpark Python, Snowpark Container Services, Cortex AI), Spark/PySpark, and MLflow or equivalent experiment tracking and model registry tools.

• Marketing Mix & NBA: Hands-on experience building Bayesian MMM (PyMC, LightweightMMM, Robyn) and Next-Best-Action recommendation engines for pharmaceutical promotional optimization.

• AI/LLM Proficiency: 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.

• HIPAA & Guardrails: Solid understanding of HIPAA de-identification standards, model explainability frameworks (SHAP, LIME), bias detection, and compliance with regulated healthcare data environments.

• Communication: Ability to translate sophisticated statistical findings into actionable recommendations for non-technical commercial stakeholders and senior leadership.

Desirable Skills/Experience:

• Rare Disease & Specialty Pharma: 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.

• Industry Data Sources: 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.

• NLP & Deep Learning: Experience with NLP (topic modeling, NER, embeddings, text classification) and neural network architectures (RNNs, LSTMs, transformers) for healthcare analytics applications.

• LLM Evaluation: Experience with RLHF concepts, benchmark design, systematic prompt evaluation, and agent reasoning quality assessment.

• Visualization: Proficiency with PowerBI, Tableau, or Qlik for executive-facing dashboards and self-service reporting.

Why AstraZeneca:

Here you will use cutting-edge data platforms and modern AI methods to tackle meaningful problems for under-served patient populations, supported by teammates who value curiosity, integrity, and momentum. You will feel the energy of a nimble, entrepreneurial environment with the reach of a global biopharma, collaborating across disciplines to turn analytics into confident decisions that shape markets and care pathways. We value kindness alongside