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Senior Manager, AI and Data Science Solution Architect
hace 2 meses
About the Role
Pfizer is seeking a highly skilled Senior Manager, AI and Data Science Solution Engineer to join our Artificial Intelligence, Data, and Advanced Analytics organization (AIDA). As a technical expert within the Data Science Industrialization team, you will be responsible for architecting and implementing AI solutions and reusable AI components that accelerate use case delivery.
Key Responsibilities
- Develop scalable and reliable AI solutions and reusable software components
- Enforce coding standards, best practices, and thorough testing 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, 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 contribute to the broader talent building framework by facilitating related trainings
- Communicate value delivered through reusable AI components to end-user functions and evangelize innovative ideas of reusable and 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 AIDA Platforms 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 AIDA Platforms 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
Requirements
- 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)
- 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
Work Location Assignment
Mexico City
