Provide scientific leadership and technical support for Pfizer clinical and non-clinical projects. This is a science-based position with limited managerial and administrative responsibilities. The applicant will work with teams in many therapeutic areas across the enterprise, on both clinical and non-clinical applications. Emphasis will be on application of artificial intelligence (AI) systems and predictive modelling techniques across the drug development continuum using structured and unstructured data sources, including image-based data, real-world data, and data from unstructured text (e.g., adverse event reporting).
The applicant is expected to develop effective collaborations with relevant stakeholders including bench scientists, study statisticians, clinicians, development operations, pharmaceutical sciences, regulatory and safety. The responsibilities and effort for the position are divided approximately equally between project-specific collaboration, longer-term methodological research to support drug development, training, and external activities.
- Support the development and implementation of techniques based on state-of-the-art research and seek creative solutions through collaborations and interactions with other experts in drug development.
- Provide external influence through collaborative partnership, scholarship and professional networking with academic, government and industry statisticians and software engineers
- Increase expertise amongst Pfizer statisticians through dissemination of new AI and machine learning methodologies and training on software to implement these approaches.
- Work closely with global and regional teams to apply AI/ML innovative solutions to business problems and deliver on improving efficiencies in areas of drug development, e.g. in drug discovery, clinical trials, operational efficiencies.
- Translate business requirements into tangible solution specifications and high quality, on time deliverables
- Effectively use tools to manipulate and create large-scale databases from internal and external sources, to enhance business use cases.
- Graduate with at least 1 year of relevant business experience or a fresh Ph. D with AI/ML/DL related qualification (Data Science, Computer Science/Engineering Statistics/Biostatistics.
- Python or R fluency
- Ability to contribute to an AI/ML/data science pipeline including the various steps of data retrieval, cleaning, analysis/modeling, application of AI algorithms through to production
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- Familiarity with standard supervised and unsupervised machine learning approaches
- Knowledge of deep learning for NLP, particularly transformer-based models such as the BERT family
- Eagerness and self-starter to leverage and apply the latest research to real problems
- Effective verbal and written communication skills in relating to colleagues and associates both inside and outside the organization.
- Ability to complement the team with new approaches such as network analysis, knowledge graphs, graph neural networks, etc. would highly desired
- Experience working in scalable computing would be a valuable supplement (AWS, Spark, Kubernetes, etc.)
- Experience in data visualization, user interfaces (Flask, Shiny, etc.), data pipelines and MLOps are a plus
- Experience as a technical consultant working between those with and without AI experience
- Knowledge of pharmaceutical area and chemistry/biology familiarity.