Senior Manager, Data Science & AI

The Data Science & AI team with Commercial Analytics & AI (CAAI) organization is committed to transforming data into actionable intelligence, empowering Pfizer to stay competitive and innovative in today's data-driven landscape.  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 Senior 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 either support a brand or therapeutic area or be part of a team designing, delivering, and upgrading innovative capabilities. 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.

RESPONSIBILITIES:

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 CAAI 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.

Product / Brand and Therapeutic Area (TA) Insights 

• Deliver advanced analytical models, predictive algorithms, and AI-powered tools to extract actionable insights to drive US Commercial strategies and tactics.

• Engage with the end-to-end delivery of data science insights, from framing the business question, designing the solution, and delivering recommendations. 

• Break down technical concepts into digestible insights and guide diverse stakeholders how to interpret.

• Continuously evaluate and enhance existing brand /TA data science capabilities, identifying opportunities for optimization and innovation to drive greater business impact and ROI.

• Build strong relationships with key stakeholders, effectively communicating the value proposition of data science and fostering a culture of data-driven decision-making.

Collaborate Cross-Functionally as a Brand/TA Focused Analytics POD

• Collaborate within the analytics POD, coordinating efforts with the Insight Strategy & Execution and Market Research Insights counterparts to develop and execute a comprehensive brand analytics plan.

• Deliver consolidated insights and actionable recommendations to US Commercial teams, ensuring alignment with strategic objectives and insights findings.

• Represent data science function and capabilities in Analytic POD meetings.

• Work closely with cross-functional teams to ensure seamless integration of brand analytics insights into decision-making processes and strategic initiatives.

Innovative Data Science Capabilities

• Support the design, delivery, and scaling of innovative solutions across the organization – from pilot phases to full-scale implementation.

• Collaborate on the delivery process, leveraging agile methodologies and best practices to efficiently progress from pilot projects to scalable solutions, while maintaining a focus on quality and innovation.

• Actively participate in upgrading and refining capabilities based on feedback and insights gathered during pilot phases, continuously enhancing the effectiveness and relevance of implemented solutions.

• Play a key role in the organizational transformation by facilitating the adoption of new capabilities at scale.

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.

QUALIFICATION & EXPERIENCE:

• Bachelor’s degree with 8-12 years of experience, preferably in Engineering, Economics, Statistics, Computer Science, or related quantitative field.

• Advanced degree with 3+ years of experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.

• Experience using Data Science models to solve problems in a business environment setting.

Relevant Experience

• Experience with both traditional SQL and modern NoSQL data stores including SQL, and large-scale distributed systems such as Hadoop and or working in Snowflake/Databricks.

• Experience with machine learning technology, such as: big data stacked Java, Python, R, Scala and visualization techniques, including Dash, Tableau and Angular.

• Experience in understanding brand content, strategy, and tactics.

• Ability to effectively utilize dashboards and data products to derive insights.

• Experience with supporting commercial strategies and tactics, experience in pharmaceutical or healthcare industry is preferred.

• Experience in management of secondary data with application of real-world data.

• Ability to partner with cross-functional teams (Commercial, Medical, Operations) to execute brand tactics.

• Able to connect, integrate and synthesize analysis and data into a meaningful ‘so what’ to drive concrete strategic recommendations for brand tactics.

• Capable of describing relevant caveats in data or in a model and how they relate to business question.

• Ability to be flexible, prioritize multiple demands and deal with ambiguity.

PROFESSIONAL CHARACTERISTICS

• Growth Mindset: Evaluates, understands and communicates the impact of certain data insights across the business and works to assist business partners foresee potential strategic changes.

• 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.

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

• Strong Communicator: 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.

• Relationship Manager: Acts as a thought partner and brings forward recommendations/questions that influences stakeholders; builds robust and long-term strategic relationships with individuals from all levels of the organization, understanding individual goals and objectives to ensure future alignment of the entire portfolio.

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

• Strong 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.

• Proactive 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

Financial Accountability

• Manage budget and spending for CAAI Data Science projects (including contract resources)

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