Senior Manager, AI and Data Science Solution Engineer

hace 6 meses

Mexico City Pfizer A tiempo completo

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 Artificial Intelligence, Data, and Advanced Analytics organization (AIDA) 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 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, and collaboration, and foster innovation. 

Role 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

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 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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