Manager, Data Science & AI

The Global Commercial Analytics (GCA) team within the Chief Marketing Office (CMO) organization is dedicated to transforming data into actionable intelligence, enabling the business to remain competitive and innovative in a data-driven world.  We play a pivotal role in extracting insights from large and complex datasets to drive strategic decision-making. Collaborating closely with various subject matter experts across various fields, our team leverages advanced statistical analysis, machine learning techniques, and data visualization tools to uncover patterns, trends, and correlations within the data. Additionally, we are dedicated to delivering new, innovative capabilities by deploying cutting-edge Machine learning algorithms and artificial intelligence techniques to solve complex problems and create value.

We are looking for a Manager, Data Science and AI who will be responsible for delivering data-derived insights and/or AI-powered analytics tools to Pfizer’s Commercial organization and will support a brand or therapeutic area. This includes leading the execution and interpretation of AI/ML models, framing problems, and shaping solutions with clear and compelling communication of data-driven insights. We are seeking a hands-on Data Scientist to design and implement advanced analytics and machine learning solutions that drive commercial decision-making in the pharmaceutical domain. The ideal candidate will have strong expertise in statistical modeling, forecasting, segmentation, clustering, classification, and regression, with experience in Bayesian methods. Familiarity with pharma or healthcare data is highly desirable. Exposure to agentic AI frameworks is a plus.

This role is dynamic, fast-paced, highly collaborative, and covers a broad range of strategic topics that are critical to our business. The successful candidate will join GCA colleagues worldwide that are driving business transformation through proactive thought-leadership, innovative analytical capabilities, and their ability to communicate highly complex and dynamic information in new and creative ways.

Key Responsibilities

• Predictive Modeling & Forecasting

  • Develop and deploy forecasting models for sales, demand, and market performance using advanced statistical and ML techniques.

  • Apply Bayesian modeling for uncertainty quantification and scenario planning.

• Segmentation & Targeting

  • Implement customer/physician segmentation using clustering algorithms and behavioral data.

  • Build classification models to predict engagement, conversion, and prescribing patterns.

• Commercial Analytics

  • Design and execute regression models to measure promotional effectiveness and ROI.

  • Support marketing mix modeling (MMM) and resource allocation strategies.

• Machine Learning & AI

  • Develop ML pipelines for classification, clustering, and recommendation systems.

  • Explore agentic AI approaches for workflow automation and decision support (good-to-have).

• Data Management & Visualization

  • Work with large-scale pharma datasets (e.g., IQVIA, Veeva CRM, claims, prescription data).

  • Create interactive dashboards and visualizations using Tableau/Power BI for senior stakeholders.

• Collaboration & Communication

  • Partner with Commercial, Marketing, and Insights teams to understand business needs.

  • Present analytical findings and actionable recommendations through clear storytelling.

Required Qualifications

• Education: Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related quantitative field.

• Experience: 5–8 years in Data Science or Advanced analytics, preferably in Commercial Pharma or Healthcare.

• Technical Skills:

  • Strong proficiency in Python (pandas, scikit-learn, statsmodels, PyMC for Bayesian).

  • SQL for data extraction and transformation.

  • Experience with ML algorithms: regression, classification, clustering, time-series forecasting.

  • Familiarity with Bayesian modeling and probabilistic programming.

  • Visualization tools: Tableau, Power BI.

• Domain Knowledge: Pharma/healthcare datasets and commercial analytics concepts.

• Soft Skills: Excellent communication and ability to translate complex analytics into business insights.

Preferred Skills

• Experience with agentic AI frameworks (good-to-have).

• Knowledge of cloud platforms (AWS, Azure, GCP) and MLOps practices.

• Exposure to marketing mix modeling (MMM) and promotional analytics.

Cross-Functional Collaboration

• Work closely with Analytics Engineering to ensure the data ecosystem is conducive for data science modeling purposes.

• Partner with Digital teams to enhance data science capabilities, aligning efforts to leverage digital data sources effectively.

• Foster collaboration with other teams to ensure seamless integration of data science initiatives across the organization's infrastructure, promoting efficiency and effectiveness in leveraging data for informed decision-making.

PROFESSIONAL CHARACTERISTICS

• Analytical Thinker: Understands how to synthesize facts and information from varied data sources, both new and pre-existing, into discernable insights and perspectives; takes a problem-solving approach by connecting analytical thinking with an understanding of business drivers and how CAAI can provide value to the organization.

• Data and Information Manager: Understands and uses analytical skills/tools to produce data in a clean, organized way to drive objective insights.

• Communication: Can understand, translate, and distill the complex, technical findings of the data science team into commentary that facilitates effective decision making; can readily align interpersonal style with the individual needs of others.

• Collaborative: Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork.

• Project Manager: Clearly articulates scope and deliverables of projects; breaks complex initiatives into detailed component parts and sequences actions appropriately; develops action plans and monitors progress independently; designs success criteria and uses them to track outcomes; drives implementation of recommendations when appropriate, engages with stakeholders throughout to ensure buy-in.

• Self-Starter: Takes an active role in one’s own professional development; stays abreast of analytical trends, and cutting-edge applications of data.

ORGANIZATIONAL RELATIONSHIPS

• US Commercial Brand/TA Teams

• Data Science and AI Leadership Team

• Close collaboration with Analytics Engineering, Insight Strategy & Execution, and Market Research Insights team

• Data Science counterparts in Digital Organization

• Chief Marketing Office across innovative data-science driven capabilities

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.