Why Patients Need You

All over the world, Pfizer colleagues work together to positively impact health for everyone, everywhere. Our colleagues have the opportunity to grow and develop a career that offers both individual and company success; be part of an ownership culture that values diversity and where all colleagues are energized and engaged and have the ability to impact health and lives of millions of people. Digital is at the core of how Pfizer delivers Breakthroughs That Change Patient’s Lives. Advanced technologies that accelerate research, development, manufacturing, and patient access to therapies are all made possible by the infrastructures that enable our Digital landscape.

What You Will Achieve

The Infrastructure & Automation organization delivers excellence in the pursuit of those breakthroughs through industry-leading service performance. We ensure optimal performance of the network and hosting services that power Pfizer's business processes. We strive to revolutionize service dependability by applying advanced analytics to drive predictive detection, identifying potential issues with our services, and intervening before they disrupt our business. We place data at the heart of what we do and apply a relentless focus on continuous improvement to enable Pfizer’s business processes and patient outcomes.

ROLE SUMMARY

We are looking for a data-driven professional with strong AI expertise to join our Performance & AI Insights team within the Infrastructure & Automation organization. In this role, you will design, develop, and operationalize machine learning and AI solutions that transform raw service and infrastructure data into predictive, actionable insights. This position is critical in leveraging advanced AI techniques to drive innovation and deliver measurable business outcomes.

ROLE RESPONSIBILITIES 

Develop and deploy advanced predictive and analytical models that deliver actionable insights to enhance service performance, scalability, and cost efficiency. These models will proactively anticipate and mitigate risks—such as service disruptions, performance degradation, capacity constraints, anomalies, compliance breaches, and reliability concerns—while enabling data-driven decisions that optimize IT operations, regulatory processes, and overall business outcomes.

Embed machine-learned, AI-driven capabilities into core business and infrastructure workflows to enhance resilience and efficiency.

Leverage modern AI/ML techniques—including supervised/unsupervised learning, time-series forecasting, anomaly detection, and Retrieval-Augmented Generation (RAG)—to improve automated decision-making across multiple domains.

Apply machine learning platforms (e.g., Dataiku, Azure ML, AWS SageMaker) to identify patterns, predict risks, and optimize KPIs for infrastructure, performance, and regulatory adherence.

Extend advanced analytics and AI techniques using graph-based analysis for dependency mapping and impact prediction.

Integrate domain-specific AI tools (e.g., ServiceNow Predictive Intelligence) to automate classification and enhance knowledge management across all business areas.

Monitor and continuously improve data quality across diverse platforms using AI-based validation and anomaly detection to ensure integrity and trustworthiness.

Partner with Data Engineers to define semantic relationships between IT assets, financial entities, and compliance controls, integrating data from CMDB, monitoring systems, cloud platforms, audit logs and other data sources.

Partner with stakeholders to identify AI integration opportunities and ensure measurable adoption across platforms.

BASIC QUALIFICATIONS

Bachelor's degree in data science, business, or related field with 5+ years of relevant experience.

Data Analysis & Machine Learning – Strong statistical analysis, predictive modeling, and ML algorithm expertise.

Proficiency in Python or R, SQL, and building scalable data pipelines.

Experience with AI/ML libraries: TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers.

Skills in feature engineering, RAG, prompt engineering and model fine-tuning.

Strong understanding of IT operations data (e.g., incidents, changes, monitoring metrics) and infrastructure KPIs. 

Experience with anomaly detection, forecasting, correlation methods, and root cause analysis using AI/ML. 

Experience with Dataiku, Azure ML, AWS SageMaker, or Google Vertex AI for scalable deployment.

Familiarity with Docker, FastAPI, and CI/CD for ML pipelines.

Experience with Python, PowerShell, REST APIs or similar tools to automate workflows. 

Experience with machine learning frameworks, Agentic AI, genAI.

Background building and managing MLOps pipelines, including automated training, deployment, monitoring, retraining and drift detection.

Exposure to AIOps concepts and tools (Dynatrace, Splunk, etc.).

Ability to work with Data Engineers to define relationships between configuration items, assets and events. 

Excellent communication skills – able to translate data into insights and insights into action. 

Comfortable working in cross-functional teams and collaborating with data engineers, platform owners, and service leads.

PREFERRED QUALIFICATIONS

Experience with Dataiku or similar low-code ML platforms. 

Knowledge of ServiceNow Predictive Intelligence and Now Assist. 

Background in infrastructure operations, service management, or performance analytics. 

Understanding ITIL, infrastructure performance metrics, and operational processes.

NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS 

Standard work schedule 

 
Work Location Assignment: Hybrid

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

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