790 – Machine Learning Engineer

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Our client is a leading research-based biopharmaceutical company. They apply science and their global resources to deliver innovative therapies that extend and significantly improve lives. Every day, colleagues work across developed and emerging markets to advance wellness, prevention,  reatments and cures that challenge the most feared diseases of our time.

ROLE SUMMARY

Pfizer’s chief digital office (CDO) leads the transformation of Pfizer into a digital powerhouse that will generate patient superior experiences which results in better health outcomes.

The Analytics Platform Engineering & Operations team, which is part of the Analytics, Data & Learning (AD&L) org within CDO is responsible for the development and management of all data and analytic assets across the enterprise – from scientific/clinical to commercial across all Pfizer geographies.

The Machine Learning Engineer will be responsible for developing and maintaining a machine learning operations (MLOps) foundation and framework as part of an enterprise analytics platform, Pfizer Insights.   Pfizer Insights will be the digital engine that brings together investments we have made into a unified experience for colleagues that take us to the next level of value creation.  It powers next generation insights by developing enterprise-grade data foundations, allowing data to flow horizontally, enabling a versatile analytics environment, and embedding insights into day-to-day work to create a digital, data-driven culture.

This role with partner with data scientists from Medical and Commercial to understand and transform machine learning models developed to solve critical business outcomes into production grade for deployment into end user sales and marketing (MarTech) solutions.  The role will be interfacing directly with both Digital and Business data engineer, data science, and product teams to understand requirements and develop assets in close collaboration with users. You will have the ability to work with talented engineers, embrace uncertainty and invent a model for enriching analysts platform experience.

ROLE RESPONSIBILITIES

• Provide best practices, guidance, and support to data science team: Data versioning, Model tracking, Experiment tracking and Code bundling for ease-of-deployment
• Covert developed data/ML pipelines into scalable pipelines based on the infrastructure available (E.g. Convert Python based data science code into PySpark/SQL for scalable pushdown execution)
• Develop continuous monitoring & training pipelines allowing model training in production by collaborating with data science team
  • Determine model performance monitoring metrics
  • Determine retraining trigger mechanism
  • Design champion/challenger model and A/B Testing
• Develop CI/CD orchestration for the data science training pipeline
• Mange the production deployments and post deployment model lifecycle management activities: Drift monitoring, Model retraining, and Model technical evaluation & business validation
• Create parameterized data science pipeline for reuse across brands
• Create modularized data science “widget” to be used across commercial analytics & analysis

 BASIC QUALIFICATIONS

• BS in computer science, data science, /or an engineering field.
• At least 5 years of experience in data and analytics field
• Strong understanding of data science development lifecycle (CRISP)
• Strong hands-on skills in software engineering (e.g., Python)
• Deep understanding of MLOps principles and tech stack (e.g. MLFlow)
• Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform
• Hands-on skills in CI/CD integration (e.g. Git Hub, Git Hub Action or Jenkins)
• Strong communication skills (written & verbal)
• Pharma & Life Science commercial functional knowledge is a plus
• Pharma & Life Science commercial data literacy is a plus
• Containerization and underlying infrastructure knowledge to support performance and resource optimization