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Popular data science title with broad skill requirements and pharma preference yields moderate applicant competition.
Core data science skills transfer across industries but pharmaceutical domain requirement raises industry-specific fit sensitivity.
Requires specific pharmaceutical domain knowledge plus Python/ML competencies, producing moderately strict filters.
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Collect, clean, and process large datasets from multiple sources to prepare for analysis.
Develop, test, and document advanced machine learning models focusing on efficiency and statistical effectiveness.
Analyze data trends and visualize findings to present to stakeholders and collaborate on business problem solutions.
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Good understanding of the US pharmaceutical landscape and its key stakeholders.
Experience or exposure to segmentation, predictive analytics, forecasting, and causal inference modeling use cases.
Proficient in Python and familiar with libraries such as Pandas, Scikit-Learn, Matplotlib, TensorFlow; knowledge of SQL and R is a plus.
Able to independently manage multiple data science tasks while maintaining strong attention to detail and a research-focused mindset.
Comfortable communicating complex analytical findings to both technical and non-technical stakeholders.
Familiarity with data visualization tools like Tableau, Power BI, or R Shiny to enhance reporting and presentation.