Data Scientist - Advanced Manufacturing Technologies (AMT)

USP•Rockville, MD

5d•$89,816 - $113,450•Onsite

About The Position

The USP Advanced Manufacturing Technologies (AMT) team seeks to continuously improve medicines through practical applications and solutions in advanced manufacturing and analytical technologies that increase medicine quality and strengthen supply chain resiliency. The AMT team helps make drugs in new ways, test the quality of drugs in new ways, and build more efficient, sustainable, and competitive industries both in the US and around the globe. To do this work, the AMT team brings together expertise in chemistry, biologics, engineering, pharmaceutical 3D printing, data science, economics, business, and regulatory science. The AMT Data Scientist is a scientific professional who leverages physical modelling and data science expertise to support scientific, supply chain, sustainability, and pharmaceutical manufacturing programs. This will include, but are not limited to, the following program areas: 1. AI/ML models to streamline pharmaceutical supply chain vulnerability and solution analyses, particularly through synthetic pathway analysis and retrosynthesis. 2. Processing and modeling spectroscopy data generated by process analytical technologies (PAT). 3. MedSuRe Climate-smart work package building baseline and improvement models of manufacturing, energy, water, waste, and resource utilization. The role emphasizes cross-disciplinary collaboration on time-sensitive externally and internally funded projects, hands-on solution development, development of proposals for new projects, and oversight of internal and external partner deliverables. The workload balance in these areas depends on external funding priorities and projects.

Requirements

Bachelor’s Degree and five (5) years of relevant experience; master’s degree and three (3) years of relevant experience; or a Ph.D. and one (1) year of relevant experience required.
Programming and computational abilities in data science languages and frameworks (e.g., Python—Pandas, scikit-learn, Matplotlib, TensorFlow, PyTorch, LangChain; R; MATLAB;).
Demonstrated experience with cheminformatics and retrosynthetic frameworks such as RDKit, SynPlanner, and AIZynthFinder.
Demonstrated experience applying machine learning and statistical techniques to complex systems such as pre-processing, classification, regression, clustering, dimensionality reduction, anomaly detection, and model selection.
Experience following good software engineering practices, designing scalable solutions, validating pipelines, and deploying solutions into production.
Demonstrated experience deploying to cloud environments and services such as AWS, Azure, or Google Cloud.
Working knowledge of organic chemistry, chemical synthesis, the pharmaceutical industry, and manufacturing.
Strong analytical reasoning, critical thinking, and troubleshooting ability.
Ability to engage productively with both internal and external stakeholders and oversee vendors.
High attention to detail and integrity.
Initiative to solve problems and develop solutions on a deadline.
Solid communications skills – both written and oral.
Desire to affect change and drive public health impact.

Nice To Haves

Preferred degrees in data science, statistics, chemistry, chemical engineering, or closely related field with significant emphasis in mathematical and statistical methods to extract meaningful information.
Familiarity with analytical techniques and platforms such as NIR, Raman, FTIR, and HPLC as well as chemometric methods to extract actionable insights.
Familiarity with the application of data science to climate risk, sustainable manufacturing, lifecycle assessment, or other environmental considerations.
Experience applying data science to model supply chains, manufacturing quality, or other manufacturing operations.
Experience defining and executing on contract work, donor-funded projects, or government projects.

Responsibilities

Independently design, create, validate, and continually refine scalable multivariate models using machine learning algorithms such as gradient boosting, partial least squares (PLS), support vector machines, and neural networks along with advanced feature engineering, validation, and explainability techniques to ensure model robustness and interpretability while managing high dimensional data sets.
Leverage AI foundational models, pipelines, and agentic techniques to extract insight, structured results, and recommendations from large and diverse chemical knowledge bases and generate analyses.
Develop and apply modelling algorithms using chemical, materials, and biologic data sets.
Act as a subject-matter expert on AI, Machine Learning, and data science on cross-functional projects, including the technical design of proposals and plans of research.
Develop and deploy scalable AL/ML tools for client and internal use within the AMT team to improve efficiency and quality of complex, technical and economic analyses.
Design and implement models within key AMT areas of interest including: Pharmaceutical supply chain and chemical synthesis; Novel drug manufacturing technologies such as flow chemistry, process analytical technologies (PAT), pharmaceutical 3D printing; Environmental impact and total lifecycle analysis of pharmaceutical manufacturing and optimization against environmental impacts, cost of production.
Maintain primary ownership over model requirements, specification, design, and implementation in one of the three key AMT areas of interest.
Ensure all work is done on time, meets client expectations, and exemplifies the trust, quality, and reliability expected from a USP solution.
Other duties as assigned.