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 Experience team, which is part of the Artificial Intelligence, Data and Advanced Analytics (AIDA) organization, is responsible for creating a seamless experience for analytics experts to harness the potential of big data, machine learning, and interactive analytics through a unified platform across the enterprise – from scientific/clinical to commercial across all Pfizer geographies.

Part of the Analytics Experience team is an Analytics Extensibility team that will be responsible for developing frameworks and tools to industrialize data science processes and enabling analytics experts to deploy machine learning models into production using the enterprise analytics platform. The enterprise analytics platform 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.

The role will be responsible for developing and maintaining a machine learning operations (MLOps) foundation and framework as part of an enterprise analytics platform. 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

• With supervision, you will be mining massive amounts of real-world healthcare, research, and business data to extract useful insights 

• With supervision you will 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 

• Setup 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 

• Become a subject matter expert on how to use the data science platform to help other teams

• Able to work in ambiguous projects and deliver analytical framework 

BASIC QUALIFICATIONS

• BS in computer science, data science, /or an engineering/quantitative field

• At least 3 years of experience in data and analytics field

• Experience/projects involving real-world analytical problems using machine learning, statistical modeling, or similar quantitative approaches

• Fluency in SQL or other programming languages (Python, Java, and/or C++)

• Some development experience in at least one scripting language (PHP, Perl, Python, etc.) 

• Some understanding of MLOps principles and tech stack (e.g., MLFlow)

• Some understanding of CI/CD integration and the data science development lifecycle

• Strong communication skills (written & verbal) 

PREFERRED QUALIFICATIONS

• Project management experience 

• A passion for problem-solving, comfort with ambiguity, and creativity 

• Ability to thrive in a dynamic and fast-paced environment and drive change, and collaborate effectively with a variety of individuals and organization 

• Hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 12 or 11) platform or similar platform

• Pharma & Life Science commercial functional knowledge is a plus

• Pharma & Life Science commercial data literacy is a plus 

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