POSITION SUMMARY

ROLE SUMMARY

The person in this role is accountable for delivering data science-driven strategic and tactical support in China. As a strategic partner to Commercial and other function teams, this person will develop and implement models and data-science derived insights that directly influence and transform most critical questions across the business. This will include leading the execution and interpretation AI/ML models, framing problems, and shaping solutions with clear and compelling communication of data-driven insights.

As a Senior Manager Data Scientist, the person in this role will work closely with the Data Science COE and shape the industrialization of bespoke AI/ML models, that directly impact the working models of other members of the GCA Data Science Team.

• Provide data science and insights to Country Commercial and Medical teams to drive brand tactic decisions
• Act as strategic partner to frame, investigate, translate complex data related models, and answer key business questions related to the identification and evaluation of Commercial and Medical brand strategies and tactics
• Lead the development and execution of data science projects, including the design and implementation of machine learning models and algorithms
• Oversee the collection, processing, and analysis of large datasets to extract actionable insights
• Ensure the quality and accuracy of data and analytics outputs through rigorous testing and validation
• Interface with Omnichannel-operations on execution and data science bespoke analytical needs
• Connect machine learning models and insights together to identify Medical and Commercial brand opportunities and tactics to execute
• Guide Medical and Commercial brand teams via compelling and persuasive story and deliver clear and actionable brand tactic recommendations
• Track and analyze impacts of Medical and Commercial brand strategies and tactics using dashboards and data products
• Stay up-to-date with the latest advancements in data science and machine learning technologies, LLMs and Generative AI

• Partner with Data Science COE to develop bespoke AI and machine learning models; Configure pre-built models and interpret updated results
• Ensure alignment with Data Science COEs teams to ensure cohesive activities with stakeholders

• Partner with GCA market lead to incorporate and prioritize data science insights into analytics plans and recommendations
• Contribute to the advancement of GCA data science and consulting capabilities; seek to share knowledge and expertise with other colleagues making use of knowledge sharing platform
• Partner with other analytic functions to advance the use of novel data sources, including RWD
• Design and implement secure and scalable infrastructure tools, data integrations, and automation using modern light-weight technologies that enable data engineering (content development) to create scaled analytics.
• Collaborate with cross-functional teams including Pfizer Enterprise IT to align, standardize, optimize, and scale infrastructure in alignment with Pfizer's standards.
• Own and refine existing continuous integration continuous delivery (CI/CD) processes for build, test, and deployment within the Commercial Data Science platform stack.
• Work closely with content development teams to streamline workflows and improve efficiency of tools for their use in the local development context, following SDLC best practices.
• Innovate to solve complex problems that require knowledge of containers, APIs, AWS and Azure cloud infrastructure, security, and best practices in infrastructure engineering.
• Develop monitoring solutions and operational process for analytics platforms, ensuring early detection and rapid response to potential issues.
• Participate in incident response activities, troubleshoot problems, and implement preventive measures such as expansion of QA frameworks.
• Collaborate with Data Science and Data Science teams to understand application requirements and provide effective infrastructure support.
• Maintain comprehensive documentation of infrastructure configurations and processes, including the DevOps runbook.
• Own coding and engineering best practices standards and governance for a multi-faceted set of ELT pipelines that operate on various cadences to satisfy business requirements.
• Implement security best practices to protect analytics environments and ensure compliance with Pfizer's stringent standards.

• Lead and build test automation tools and frameworks to test pipelines.
• Partner with enterprise Digital Engineering team to identify or code APIs that need to be instrumented for data analytics and reporting and align with already established data pipelines.

• Ramp up quickly in understanding data sources required for data products and the goals of Commercial Analytics in a Pharma business.
• Collaboration with the Data Science Director to work hand in hand with the Platform/Digital and DevOps team to specify platform requirements, tool enhancements, observability & monitoring, security/audit, and alerting on various data pipelines and jobs as needed for operation the Commercial Data Science organization.

• Solid Line to: Director, China Data Science & AI Lead

• Master's or Ph.D. in Data science, computer science, statistics, engineering, data science, or related applicable field.
• 7+ years of experience using data science or advanced analytics solving real business problems, working in an agile development team to design and develop machine learning and AI solutions.
• Recent Healthcare Life Sciences (pharma preferred) and ecosystem professional industry experience is preferred, commercial/marketing experience is a plus.

• Strong proficiency in programming languages such as Python, SQL, and/or R.
• Experience with matching learning frameworks and tools such as TensorFlow, Pytorch, and Scikit-learn.
• Knowledge and practical experience of Statistical methods and A/B testing experimentation method.
• Knowledge and practical experience of Machine learning, Deep learning, and Time series prediction.
• Solid understanding of data management and big data processing technologies (e.g., Apache Spark, Schema design, Data Pipeline, Workflows).
• Strong data storytelling and stakeholder management abilities.
• Excellent problem-solving skills and the ability to think critically and analytically.
• Professional hands-on experience with containers (e.g., Docker, ECS, Kubernetes), cloud platforms (e.g., AWS, Azure), and event-driven architecture is required.
• Advanced experience working in multi-stage environments that leverage automation such as Step Functions, Airflow, Cron, etc.
• Must demonstrate ability to leverage digital diagraming tools and communicate technical requirements in a remote setting.
• Must have experience working in Jira and Confluence, and be a strong writer, contributing to engineering team documentation/playbooks.
• Experience in project management and stakeholder engagement to drive impact.
• Experience in developing QA automation frameworks, functional and non-functional, is a plus.
• Experience with cloud platforms such as AWS, Azure, or Dataiku is a plus

• Knowledge of containerization and orchestration tools such as Docker and Kubernetes.
• Experience with big data technologies such as Snowflake, Hadoop, Spark, and Hive.
• Familiarity with data visualization tools such as Tableau , Power BI, Webapp.
• Experience with large language models (LLMs) and generative AI technologies.

• Project Management: Overseeing complex, cross-functional projects, ensuring delivery on time and within budget.
• Technical Strategy: Contributing to the technical strategy and architecture decisions, ensuring scalability and performance of data solutions.
• Collaboration: Influencing their data engineering team, working closely with other departments, understanding their data needs, and delivering solutions that meet these needs.
• Operational Excellence: Ensuring the reliability, efficiency, and quality of data services andpipelines.
• Change Management: Leading change initiatives, improving processes, and implementing new technologies.