Senior Manager, AI and Data Science Solution Architect

hace 2 semanas

Ciudad de México, Ciudad de México Pfizer A tiempo completo

Role Summary

Do you want to make a significant impact on patient health worldwide? Do you thrive in a fast-paced environment that brings together scientific, clinical, and commercial domains through engineering, data science, and AI?

Then you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our colleagues, patients, and physicians at Pfizer Digital's Artificial Intelligence, Data, and Advanced Analytics organization (AIDA).

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 Data Science Industrialization team within Data Science Solutions and Initiatives 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 Data Science 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, collaboration, and foster innovation.

Key Responsibilities

  • Develop scalable and reliable AI solutions and reusable software components.
  • 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.
  • 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 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.

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).
  • 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

EEO (Equal Employment Opportunity) & Employment Eligibility

Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, or disability.

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