Manager, Data Science and AI

Role Summary The Global Commercial Analytics (GCA) team within the organization is dedicated to transforming data into actionable intelligence, enabling the business to remain competitive and innovative in a data-driven world. The Manager, Data Science & AI, will play a pivotal role in extracting insights from large and complex datasets to drive strategic decision-making. By collaborating closely with subject matter experts across various fields, the candidate will leverage advanced statistical analysis, machine learning techniques, and data visualization tools to uncover patterns, trends, and correlations within the data. The Manager is responsible for leading high-impact projects delivering new, innovative capabilities by deploying cutting-edge machine learning algorithms and techniques to solve complex problems and create value. This role is dynamic, fast-paced, highly collaborative, and covers a broad range of strategic topics that are critical to the pharma business. Roles & Responsibilities Project Leadership: Leads and implements the delivery and all aspects of engineering activities for data and analytic platforms/solutions. Collaboration: Works closely with cross-functional teams and business stakeholders to develop roadmaps and shape solutions using standard technology stacks. Building Analytics Pipelines: Designs and oversees the development of analytics pipelines, products, and insights for the Commercial Pharma domain. Advanced Statistical Analysis: Utilizes advanced statistical analysis, machine learning techniques, and data visualization tools to uncover patterns, trends, and actionable insights from data. Business Impact: Synthesizes analysis and data into meaningful recommendations to drive concrete strategic decisions for brand tactics and commercial strategy. Innovation: Stays abreast of analytical trends and cutting-edge applications of data science and AI, championing the adoption of new techniques and tools. Quality & Governance: Ensures best practices in data management, model validation, and ethical AI, maintaining high standards of quality and compliance. Qualification & Experience Bachelor?s, Master?s, or PhD in Computer Science, Statistics, Data Science, Engineering, or a related quantitative field. 6?9 years of experience in Data Science, Analytics, or AI/ML roles, preferably in commercial pharma, healthcare, or related industries. Proficiency in programming languages such as Python, with exposure to OOPS concepts, Streamlit, Machine Learning, Deep Learning and data visualization tools (including Plotly, Matplotlib, Seaborn, Tableau/PowerBI etc.). Familiarity with databases such as SQL and NoSQL, and experience with data manipulation libraries (e.g., Pandas, Numpy) and machine learning frameworks (e.g., scikit-learn, PyTorch). Experience with large-scale distributed systems (e.g., PySpark, Snowflake). Strong understanding of statistical analysis, hypothesis testing, and other advanced statistical analytics techniques. Exposure to modern software development workflows (Git, CI/CD, Docker). Story boarding and experience with MS Office, Sharepoint Good to Have: Familiarity with HCP engagement data, sales data, CRM platforms (e.g., Veeva), and omnichannel promotional metrics. Experience in dashboard development, story boarding, and data product management. Ability to describe relevant caveats in data or models and how they relate to business questions. Experience with pharma data and commercial analytics is highly desirable. Track record of delivering business impact through analytics in pharma or related sectors. Professional Characteristics: Adaptability and Flexibility: Demonstrates the ability to adjust and thrive in changing environments, embracing new tasks, and applying knowledge in diverse contexts. Strong Communication: Exhibits effective communication skills for workplace interactions, including conveying ideas clearly and listening actively. Positivity: Maintains a positive attitude, showing a willingness to work hard and learn, contributing to a harmonious work environment. Self-Starter: Takes an active role in professional development; stays abreast of analytical trends and cutting-edge applications of data. Work Location Assignment: Hybrid Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates. Marketing and Market Research