Brand Analytics Director

AstraZeneca•Wilmington, DE

3d•$155,517 - $233,275

About The Position

The US Brand Analytics Director is the brand’s expert and strategic authority on commercial data and analytics. The role provides an end-to-end view of performance across patient, HCP, account, access, field and market dynamics, and delivers a competitive advantage through rigorous analysis and insight that supports US and Global decision-making. The Director continuously measures performance against strategy, forecast and key business objectives, diagnoses the drivers behind results, and proactively identifies emerging opportunities, risks, unmet needs and growth levers. As a standing strategic advisor to Brand Leadership Teams, the Director brings an independent, evidence-based point of view to major business choices, challenges assumptions, connects signals across research, secondary data, competitive intelligence and forecasting, and converts analytics into clear recommendations and actions. This is a hands-on analytical role and a proactive internal consultant to the brand team. The Director sets the analytics agenda, ensures leaders have the right KPIs and decision-support tools, and advances scalable analytics, AI and automation capabilities that improve the speed and quality of commercial decisions. The role partners closely with the Senior Director, Insights, Analytics & Forecasting. It has no direct reports but may manage contractors and consultants to complete analytical projects. The role’s principle is to own the business question and recommendation, not simply the requested output. It is accountable for US analytics strategy and a prioritized analytics plan; source-of-truth performance views, KPIs and diagnostic narratives, including weekly and monthly performance tracking with executive-level summaries of gaps versus forecast and their drivers; and decision-ready recommendations that improve brand execution and investment choices.

Requirements

At least 5 years of experience applying advanced methods and statistical procedures to large and disparate datasets.
Experience with statistical analysis and modelling, including design of experiments, time series, regression, applied econometrics and Bayesian methods.
At least 2 years of recent experience and proficiency with Python, R, SQL and big data technology (Hadoop ecosystem, Spark) or equivalent cloud platforms such as Databricks.
Working knowledge of data visualization tools such as Power BI, Tableau, MicroStrategy, Qlikview, D3js or similar.
Proficiency in Excel and PowerPoint.
Working experience with generative AI and AI-assisted analytics tools, such as Databricks Genie and Copilot, and a track record of applying them to improve analytical speed, quality or automation.
Proficiency in manipulating and extracting insights from large longitudinal data sources, such as claims, EMR and other patient-level datasets.
Expertise in managing and analyzing a range of large secondary transactional databases.
Quantitative Bachelor’s degree from an accredited college or university in Engineering, Operations Research, Management Science, Economics, Statistics, Applied Math, Computer Science, Data Science or a related field.
At least 2 years of experience in pharmaceutical, biotech or healthcare analytics or secondary data analysis.

Nice To Haves

An advanced degree (Master’s, MBA or PhD) is preferred.
Strong leadership and interpersonal skills, with demonstrated ability to work collaboratively with many business leaders and cross-functional partners.
Strong communication and influencing skills, with demonstrated ability to present succinct, compelling reviews of independently developed analyses that carry insight, business implications and actions.
Strategic and critical thinking, with the ability to build and maintain credibility with the Commercial Leadership Team.
Strong organizational and time management skills and ability to manage a diverse range of simultaneous projects.
Experience leading AI or automation pilots and scaling them into governed, repeatable team capabilities; familiarity with responsible AI, model validation and change leadership.

Responsibilities

Develop a deep understanding of internal customers’ data and analytical needs, with a focus on customer-facing model leadership.
Design and implement analytical solutions for business opportunities, customer segmentation and targeting, HCP and patient opportunity identification, return-on-investment analytics, optimal resource and budget allocation across Sales and Marketing channels, patient and prospective analytics, smart business experiments (test and learn), and market simulation and patient flow modelling.
Coordinate the procurement of syndicated and custom, primary and secondary datasets.
Champion the use of local customer insight in strategic and resourcing decisions across the business.
Collaborate with Commercial, Sales Force Operations and Market Access teams on key business processes and joint initiatives.
Identify and prioritize enhancements and new analytic capabilities, and lead their development, deployment and embedding across the organization, including AI and automation use cases.
Instill a culture of continuous improvement, testing and deployment of new capabilities, and develop innovative analytical practices that sustain competitive advantage.
Summarize and present findings through various communication channels, with clear implications and recommended actions for senior leaders.
Apply deep technical knowledge shared across the functional area, such as key datasets and modelling capabilities, and understand how this work integrates with that of colleagues.
Monitor the external environment for leading analytic capabilities, inside and outside pharma, that can be applied within the organization.
Embed AI into day-to-day analytics using tools such as Databricks Genie, Genie Code and Copilot to accelerate analytical code development, model building and deliverable creation.
Build and scale AI-enabled analytics, such as AI-assisted HCP segmentation and scoring and AI-drafted weekly performance narratives, validating and interpreting outputs and turning them into recommended actions.
Define repeatable, governed AI use cases with measurable time and quality benefits, and partner with Data and Digital, Data Engineering and enterprise AI teams on governed data feeds and automation.
Apply responsible AI practices by validating AI outputs, documenting model logic and maintaining human review of insights and recommendations before they reach business leaders.
Measure performance against strategy, forecast and key business objectives; diagnose performance drivers and identify emerging opportunities, risks, unmet needs and growth levers.
Set the analytics agenda and provide source-of-truth performance views, KPIs, decision-support tools and executive-level performance summaries.
Advise Brand Leadership Teams with evidence-based recommendations and a point of view on business choices, execution and investment.
Manage external contractors and consultants as needed to complete analytical projects.