Manager, GTMx & Customer Analytics - US Hospital

** Role Summary
** The Manager, GTMx & Customer Analytics delivers hands-on analytics to inform go-to-market (GTMx) decisions across the US Hospital business. This individual will pull, engineer, and operationalize data from enterprise warehouses, build reproducible analytics workflows in Python/SQL, and partner closely with marketing, sales, and customer-facing strategy teams to measure GTMx impact, maintain scorecards/trackers, and deliver actionable insights. In addition, the manager will perform ad-hoc analyses to address emerging business questions and support strategic initiatives as needed.

The role is a key member of the hospital analytics team.
** Key Responsibilities**
* ** Data Acquisition, Engineering, and Operationalization**  + Build and maintain data pipelines: ingest, cleanse, join, and model datasets from enterprise data warehouses (e.g., commercial, customer, contracting, omnichannel, call activity) to create analysis-ready tables/views for GTMx use cases.  + Develop reproducible Python/SQL notebooks & jobs (version-controlled) for team use; implement data quality checks.  + Support creation of feature stores / curated datasets for predictive models and scorecards aligned to hospital KPIs.
* ** GTMx Analytics & Measurement**  + Translate GTMx hypotheses into measurable constructs; assist in designing A/B tests, KPI frameworks, and analyses to quantify customer impact.  + Build and maintain scorecards, trackers, and dashboards (e.g., Power BI) for GTMx performance at account/segment levels; automate refresh and alerts.  + Collaborate with Customer-Facing Strategy & Deployment to support definitions, targets, and thresholds for GTMx KPIs.
* ** Targeting, Segmentation, and Predictive Analytics**  + Develop customer segmentation using statistical and ML techniques (e.g., clustering, uplift modeling) to improve targeting and next-best-action.  + Run predictive analytics (propensity, churn risk, demand forecasting) that inform GTMx tactics and portfolio pull-through.  + Test and evaluate new customer data assets prior to operational rollout; document performance and integration recommendations.
* ** Insight Delivery & Cross-Functional Partnership**  + Convert analyses into clear, decision-oriented narratives for sales, marketing, and leadership (written memos and PowerPoint visualizations).  + Collaborate with portfolio marketing, sales operations, and account teams to operationalize insights (playbooks, targeting lists, cadence, enablement materials).  + Support launch readiness and in-market optimization for priority brands through analytics sprints and KPI tracking.
* ** Ways of Working & Excellence**  + Uphold analytics craftsmanship: code reviews, documentation, reproducibility, and governance.  + Adhere to data privacy and compliance standards when handling customer and commercial data; ensure secure access patterns and approved sharing.
** Tech Stack**
* ** Cloud Data Warehousing:
** Snowflake (primary), AWS S3 (secondary)
* ** Data Science & Analytics:
** Dataiku DSS, Python (pandas, numpy, scikit-learn), SQL
* ** Workflow & Transformation:
** Apache Airflow, dbt
* ** Visualization & Reporting:
** 1. Tableau
2. Dataiku
* ** Self-Service Analytics:
** Alteryx Designer
* ** Version Control:
** Git Hub, Collibra
* ** Security & Compliance:
** MFA, Programmatic Access Tokens, GDPR/HIPAA
* Candidates should demonstrate hands-on experience with Snowflake, Dataiku DSS, Python, and Power BI, and be comfortable working in a hybrid cloud/data environment. Familiarity with workflow orchestration (Airflow), data modeling (dbt), and BI tools (Tableau, Alteryx) is preferred.

Experience with Pfizer’s analytics platforms or similar enterprise environments is a plus.
*** Qualifications
* *** Required
* ** BA/BS in a quantitative field (e.g., Statistics, Data Science, Economics, Computer Science)
* 4+ years in commercial analytics, data science, or sales/marketing analytics (pharma/biotech/med-tech preferred) with demonstrated hands-on data engineering and modeling experience
* Strong Python (pandas, numpy, scikit-learn, Snowflake Python Connector), SQL (analytic functions, optimization), and data-pipeline skills…