Manager, Machine Learning Engineer
Full Time
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Job Type
Full-time
Full Job Description
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 Analytics & Data org within Digital Health, Medicines, and Artificial Intelligence, is responsible for the 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, 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.
The role will be responsible for developing and maintaining a machine learning operations (MLOps) foundation and framework as part of an enterprise analytics platform, Pfizer Insights. 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.
The person in this position will need manage a feature roadmap tools and frameworks in partnership Analytical Experience leads and other Data and Analytics teams to ensure identified value can be developed and delivered. Tough decisions will need to be made to balance the needs of multiple, differing stakeholders with competing priorities.
ROLE RESPONSIBILITIES
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Mining massive amounts of real-world healthcare, research, and business data to extract useful insights
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Capturing and analyzing data from new and novel data sources
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Provide best practices, guidance, and support to data science team: Data versioning, Model tracking, Experiment tracking and Code bundling for ease-of-deployment
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Assist in creation and conversion of 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)
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Develop continuous monitoring & training pipelines allowing model training in production by collaborating with data science team
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Determine model performance monitoring metrics
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Determine retraining trigger mechanism
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Design champion/challenger model and A/B Testing
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Develop CI/CD orchestration for the data science training pipeline
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Manage the production deployments and post deployment model lifecycle management activities: Drift monitoring, Model retraining, and Model technical evaluation & business validation
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Create parameterized data science pipeline for reuse across brands
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Create modularized data science “widget” to be used across commercial analytics & analysis
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SME for data science on matrix work teams
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Able to work in ambiguous projects and deliver analytical framework
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Develops project plan including timelines and milestones
BASIC QUALIFICATIONS
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BS in computer science, data science, /or an engineering/quantitative field.
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5-8 years of experience in data and analytics field
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Experience/projects involving real-world analytical problems using machine learning, statistical modeling, or similar quantitative approaches
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Fluency in SQL or other programming languages (Python, Java, and/or C++)
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Some development experience in at least one scripting language (PHP, Perl, Python, etc.)
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Some understanding of MLOps principles and tech stack (e.g., MLFlow)
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Some understanding of CI/CD integration and the data science development lifecycle
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Strong communication skills (written & verbal)
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Hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform or similar platform
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Project management experience
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A passion and proven ability for problem-solving, comfort with ambiguity, and creativity
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Ability to thrive in a dynamic and fast-paced environment and drive change, and collaborate effectively with a variety of individuals and organization
PREFERRED QUALIFICATIONS
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Pharma & Life Science commercial functional knowledge is a plus
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Pharma & Life Science commercial data literacy is a plus
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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