Associate, Data Scientist

ROLE SUMMARY

The Clinical AI/ML and Informatics team is opening up an exciting opportunity to recruit an Associate to conduct data engineering and AI/ML analysis to generate critical data-driven insights to inform clinical trial design. Key therapeutic areas of interest include the development of next-generation treatments for Diabetes and Obesity and Vaccines. The associate will play a crucial role in framing the research question, analyzing complex medical data, identifying patterns, and predicting trends. This information will be instrumental in influencing the design of future clinical trials and is a significant step towards harnessing the power of data science in the fight against the pandemic.

The associate is responsible to assist Pfizer’s AI/ML, Quantitative and Digital Sciences (AQDS) staff to leverage both internal and external data assets to deliver business insights via Artificial Intelligence (AI), statistical, and other analytic methods by collaborating with cross-functional teams within the organization with an overall goal of enhancing tactical and strategic decision making. This role requires a good understanding of technical and business aspects of Machine Learning, Statistics, Clinical Trial data, and Real World Data. Must be able to understand the analytical needs of business users, to translate the business needs into automation solutions. Requires full Data Science life cycle development experience, including data discovery and engineering, assisting tech team(s) to create hypothesis and selecting models, and test and evaluate models. Should have a strong understanding of relational databases and Clinical Trial data to facilitate data wrangling, exploration of data, and model construction and operationalization. The associate must be able to drive tangible business value, working under the direction of AQDS Lead by collaborating with other project leads, analysts, and scientific and clinical partners.

ROLE RESPONSIBILITIES

General:

• Assist the AQDS lead in implement global strategies, initiatives, processes, and standards to ensure consistent, efficient, and quality Data Science processes to meet expected quality, timelines and deliverables.
• Work on data science projects end-to-end from inception to insights, including concept development, data identification & engineering, clinical informatics, AI/ML modeling, software quality checks, and documentation, often in a iterative fashion by addressing feedbacks from the broader team.
• Run SQL queries to extract Operational, Clinical data and Specifications from various information systems, & independently assess and analyze data quality
• Prepare data including labelling of data for Machine Learning experiments and provide feedback on design of model and analytics.
• Able to pilot and build POC’s to demonstrate and test business value of Machine Learning models, understand Model metrics, iteratively improve Model performance working with tech leads and vendors.
• Analyze, interpret, and summarize Machine Learning model results using AI/ML, statistics, or other analytics methods.
• Operationalize, document, and maintain the analytic pipeline developed.
• Develop presentations and trainings for scientific and clinical stakeholders to support drug development.

BASIC QUALIFICATIONS:

• Bachelor’s degree or above or equivalent experience in a scientific or business related discipline required; Degree in Informatics, or Computer science, or Statistics, or related quantitative sciences degree with equivalent experience preferred
• 1 - 3 years of work experience post Bachelor’s degree, or a Master’s degree of the above disciplines.
• SQL experience with relational databases like Postgres or Oracle or Microsoft.
• Demonstrated experience with Python
• Understanding of Machine learning and Statistics
• Works independently, receives instruction primarily on unusual situations
• Ability to organize tasks, time and priorities. Able to manage multiple tasks simultaneously and react to problems quickly.
• Ability to communicate with internal & external stakeholders in a clear and logical fashion.

PREFERRED QUALIFICATIONS:

• Demonstrated experience with Python and/or R, SAS and Machine Learning model implementation
• Prior experience working with Clinical Trials data and Real-World Data in large or midsize companies preferred.
• Prior experience working with omics data.
• Excellent verbal and written communication skills.
• Must have solid practical understanding of classical machine learning as well as Deep Learning methods, NLP and Knowledge Graphs
• Must be a team player, with good listening abilities, self-motivated and demonstrate results.
• Prior working experience with teams spread across different time zones is a plus.

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