US Brand Analytics Director

AstraZeneca•Wilmington, DE

1d•Hybrid

About The Position

The US Brand Analytics Director drives the analytics agenda for US brand strategy from pre-launch through growth, using complex healthcare data to guide investment, prescriber and patient engagement, access, and performance decisions. In a player-coach role, the Director partners with Commercial, Medical, Market Access, Sales, Finance, Forecasting, and Insights to translate business questions into rigorous, decision-ready analytics. The role integrates patient-level, claims, EMR, prescription, affiliation, research, and forecasting data to identify opportunities, quantify risks, and inform resource allocation. The Director leads analytics for market definition, opportunity assessment, segmentation and targeting, KPI design, performance baselines, and post-launch measurement. They develop patient journeys and treatment-pathway analyses, optimize HCP segmentation and call plans, and apply machine learning, AI, and generative AI where these improve business impact, speed, and decision quality. The position also develops centralized repositories and scalable self-service capabilities, pilots and scales valuable data sources and tools, maintains a prioritized analytics roadmap, and manages external partners and secondary data investments. The role guides internal and offshore resources, contractors, and vendors, setting expectations for quality, timelines, and business relevance. The position is expected to work in person at least three days per week on average, with flexibility balanced against that office expectation.

Requirements

At least 7 years of experience applying advanced analytical methods and statistical procedures to large, disparate datasets.
Experience with statistical analysis and modeling, including Design of Experiments, Time Series, Regression, Applied Econometrics, and Bayesian methods.
Experience with data mining, predictive modeling, and machine learning algorithms.
At least 3 years of recent experience and proficiency with Python, R, SQL, and big data technologies, including the Hadoop ecosystem and Cloudera tools such as Impala, Hive, HBase, Spark, and MapReduce.
Demonstrated pharmaceutical, biotechnology, healthcare, payer, or closely related commercial analytics experience, with an understanding of analytics in brand strategy, launch readiness, and performance management.
Working knowledge of data visualization tools such as MicroStrategy, Tableau, Power BI, QlikView, D3.js, or similar.
Proficiency in Microsoft Office applications, particularly Excel and PowerPoint.
Demonstrated expertise managing, analyzing, and extracting insights from large longitudinal and transactional healthcare datasets, including claims, EMR, prescription, and other patient-level data.
A quantitative bachelor's degree from an accredited college or university in Engineering, Operations Research, Management Science, Economics, Statistics, Applied Mathematics, Computer Science, Data Science, or a related field.

Nice To Haves

An advanced degree, such as a Master's, MBA, or PhD.
Strong leadership, interpersonal, communication, and influencing skills, including the ability to collaborate with senior business leaders and cross-functional partners and translate complex analyses into succinct insights and recommended actions.
Experience scaling self-service analytics, reusable data products, automation, or responsibly governed AI and generative AI solutions.
Strong strategic and critical thinking, organizational discipline, and the ability to work effectively in a complex, fast-moving, and ambiguous environment.

Responsibilities

Synthesize commercial performance into concise narratives that clarify growth drivers, risks, and trade-offs, and elevate insights into action for senior leaders.
Lead pre-launch and launch-readiness analytics, including market definition, opportunity assessment, segmentation and targeting, KPI design, performance baselines, and post-launch measurement.
Build longitudinal patient journeys and treatment-pathway analyses to refine positioning, guide investment choices, and inform forecasting assumptions.
Lead HCP-level segmentation and optimize call plans, applying advanced machine learning where it improves impact and precision.
Use syndicated, claims, and EMR data to generate timely insights and design centralized repositories that reduce inconsistencies and enable self-service.
Drive adoption of AI and generative AI solutions, including automated insight generation and predictive modeling.
Pilot new data sources and tools using test-and-learn rigor and scale capabilities that demonstrate business value and responsible innovation.
Maintain a transparent analytics roadmap, prioritizing work based on strategic value, decision impact, and data readiness.
Manage external partners and secondary data investments, aligning spend with commercial priorities and measurable outcomes.
Guide internal and offshore resources, contractors, and vendors, setting standards for quality, timelines, and business relevance across the analytics portfolio.