ROLE SUMMARY Do you want to make an impact on patient health around the world? Do you thrive in a fast-paced environment that brings together scientific, clinical, and commercial domains through engineering, data science, and AI? Then join Pfizer Digital’s Commercial Creation Center & CDI organization (C4) where you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our colleagues, patients and physicians. Our collection of engineering, data science, and AI professionals are at the forefront of Pfizer’s transformation into a digitally driven organization that leverages data science and AI to change patients’ lives. The Commercial AI Industrialization team is a critical driver and enabler of Pfizer’s digital transformation, leading the process and engineering innovation to rapidly progress early AI and data science applications from prototypes and MVPs to full production. As a Senior Manager, AI and Data Science Solution Engineer, you will be a technical expert within the Commercial AI Industrialization team charged with architecting and implementing AI solutions and reusable AI components. You will identify, design, iteratively develop, and continuously improve reusable components for AI that accelerate use case delivery. You will implement best practices and maintain standards for AI application and API development, data engineering and data pipelining, data science and ML engineering, and prompt engineering to enable understanding and re-use, drive scalability, and optimize performance. In addition, you will be responsible for providing critical input into the AI ecosystem and platform strategy to promote self-service, drive productization, and collaboration, and foster innovation. ROLE RESPONSIBILITIES Architect and implement AI and ML solutions and reusable software components within the AWS cloud infrastructure. Ensure solutions meet the diverse needs of various use cases. As a tech lead, enforce coding standards, best practices, and thorough testing (unit, integration, etc.) to ensure reliability and maintainability Define and implement robust API and integration strategies to seamlessly connect reusable AI components with broader systems Define and implement robust technical strategies in areas such as API integration to connect reusable AI components with broader systems, industrialized AI accelerators, and the delivery of scalable AI solutions Demonstrate a proactive approach to identifying and resolving potential system issues Train and guide junior developers on concepts such as data analytics, machine learning, AI, and software development principles, tools, and best practices Foster a collaborative learning environment within the team by sharing knowledge and expertise Act as a subject matter expert for solution engineering on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for software development needs Direct research in areas such as data science, software development, data engineering and data pipelines, and prompt engineering, and contribute to the broader talent building framework by facilitating related trainings Communicate value delivered through reusable AI components to end user functions (e.g., Chief Marketing Office, PBG Commercial and Medical Affairs) and evangelize innovative ideas of reusable & scalable development approaches/frameworks/methodologies to enable new ways of developing AI solutions Provide strategic and technical input to the AI ecosystem including platform evolution, vendor scan, and new capability development Partner with AI use case development teams to ensure successful integration of reusable components into production AI solutions Partner with cross-functional team on end to end capability integration between enterprise platforms and internally developed reusable component accelerators (API registry, ML library / workflow management, enterprise connectors) Partner with cross-functional team to define best practices for reusable component architecture and engineering principles to identify and mitigate potential risks related to component performance, security, responsible AI, and resource utilization BASIC QUALIFICATIONS Bachelor’s degree in AI, data science, or computer engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline) 7+ years of work experience in data science, analytics, or solution engineering, with a track record of building and deploying complex software systems Recognized by peers as an expert in data science, AI, or software engineering with deep expertise in data science or backend solution architecture, and hands-on development Expert knowledge of backend technologies; familiar with containerization technologies like Docker; understanding of API design principles; experience with distributed systems and databases; proficient in writing clean, efficient, and maintainable code Strong understanding of the Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP) Demonstrated experience interfacing with internal and external teams to develop innovative AI and data science solutions Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc) Highly self-motivated to deliver both independently and with strong team collaboration Ability to creatively take on new challenges and work outside comfort zone Strong English communication skills (written & verbal) PREFERRED QUALIFICATIONS Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline Experience in solution architecture & design Experience in software/product engineering Strong hands-on skills in ML engineering and data science (e.g., Python, R, SQL, industrialized ETL software) Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms Experience in CI/CD integration (e.g. GitHub, GitHub Actions or Jenkins) Deep understanding of MLOps principles and tech stack (e.g. MLFlow) Experience with Dataiku Data Science Studio Hands on experience working in Agile teams, processes, and practices