Senior Data Scientist

USP•Rockville, MD

1d•Remote

About The Position

The U.S. Pharmacopeial Convention (USP) is an independent scientific organization that collaborates with the world’s leading health and science experts to develop rigorous quality standards for medicines, dietary supplements, and food ingredients. At USP, we believe that scientific excellence is driven by a commitment to fairness, integrity, and global collaboration. This belief is embedded in our core value of Passion for Quality and is demonstrated through the contributions of more than 1,300 professionals across twenty global locations, working to strengthen the supply of safe, high-quality medicines worldwide. At USP, we value inclusive scientific collaboration and recognize that attracting diverse expertise strengthens our ability to develop trusted public health standards. We foster an organizational culture that supports equitable access to mentorship, professional development, and leadership opportunities. Our partnerships, standards, and research reflect our belief that ensuring broad participation in scientific leadership results in stronger, more impactful outcomes for global health. USP is proud to be an equal employment opportunity employer (EEOE) and is committed to ensuring fair, merit-based selection processes that enable the best scientific minds—regardless of background—to contribute to advancing public health solutions worldwide. We provide reasonable accommodations to individuals with disabilities and uphold policies that create an inclusive and collaborative work environment. Brief Job Overview Use exploratory data analysis to spot anomalies, understand patterns, test hypotheses, or check assumptions. Apply various Machine Learning (ML) techniques to perform classification or regression tasks to drive business impact and address identified needs in an agile manner. Utilize natural language processing techniques to extract information and improve business workflows. Interpret and communicate results clearly and concisely to audiences with varying backgrounds and degrees of technical understanding. Develop and define data science workflows appropriate for projects and the team. Partner with internal stakeholders to identify opportunities where machine learning can add value. Collaborate with other data scientists, data engineers, and IT team to help ensure project delivery and success. Present Insights and Data Model results to business and scientific stakeholders.

Requirements

Master’s degree or foreign equivalent degree in Information Systems Management, Information Technology, Business Analytics, Data Science, Mathematics, Computer Science, or a related field, and three (3) years of related work experience.
Must have three (3) years of experience with/in: Data Science, advanced analytics, machine learning, and statistics
Building and deploying machine learning models using xgboost and decision trees
Using statistical techniques for forecasting
Advanced technical proficiency in the following: python/packages, pandas, numpy, regex, scikit-learn, xgboost, visualization packages (such as matplotlib, seaborn, or plotly), and SQL
Building dashboards using Tableau or similar platform
Data extraction and scraping
Translating stakeholder needs into technical project outputs
Ecommerce Analytics on online store fronts, inventory, and item category.
Must have two (2) year of experience with/in : Utilizing natural language processing techniques for text classification.

Responsibilities

Use exploratory data analysis to spot anomalies, understand patterns, test hypotheses, or check assumptions.
Apply various Machine Learning (ML) techniques to perform classification or regression tasks to drive business impact and address identified needs in an agile manner.
Utilize natural language processing techniques to extract information and improve business workflows.
Interpret and communicate results clearly and concisely to audiences with varying backgrounds and degrees of technical understanding.
Develop and define data science workflows appropriate for projects and the team.
Partner with internal stakeholders to identify opportunities where machine learning can add value.
Collaborate with other data scientists, data engineers, and IT team to help ensure project delivery and success.
Present Insights and Data Model results to business and scientific stakeholders.