Machine Learning Engineer, Digital Products

**Role Overview:** As a ML Engineer at U.S. Pharmacopeial Convention (USP), you will be a crucial part of the Digital product engineering team. Your expertise in building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities will play a key role in supporting projects aligned with USP's mission to protect patient safety and improve global health. You will be responsible for implementing scalable data collection, storage, and processing ML pipelines, optimizing data models and pipelines to support self-service analytics tools, and collaborating with data scientists to operationalize machine learning models. **Key Responsibilities:** - Implement scalable data collection, storage, and processing ML pipelines to support enterprise-wide data needs. - Build and optimize data models and pipelines for self-service analytics tools like Tableau, Looker, and Power BI. - Collaborate with data scientists to integrate machine learning models into production data pipelines for scalability and performance. - Build and maintain data pipelines for structured, semi-structured, and unstructured data. - Support ETL/ELT workflows and data ingestion using batch, streaming, and API-based approaches. - Develop and optimize data processing jobs using Python and distributed frameworks. - Participate in building, training, and deploying machine learning models and data-driven applications. - Create dashboards and visualizations for analytics and business reporting. - Assist in deploying data and ML workloads to cloud environments following best practices. - Collaborate with senior engineers and cross-functional teams to understand requirements and deliver solutions. - Write clean, maintainable, and well-documented code, and participate in code reviews. - Continuously learn and apply best practices in data engineering, ML engineering, and MLOps. **Qualifications Required:** - Bachelors degree in a relevant field (e.g. Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience. - Strong communication skills: Verbal, written, and interpersonal. - Introductory knowledge of GenAI/LLM concepts (prompting, embeddings, RAG basics) is a plus. - Academic or personal projects involving dashboards, analytics, ML models, or cloud deployments. - Familiarity with data lakes or warehouses (AWS S3, Redshift, Snowflake, Delta Lake). - Basic experience building APIs using FastAPI, Flask, or Django. **Additional Details:** USP is committed to creating an inclusive environment where every employee feels fully empowered and valued, regardless of personality, race, ethnicity, abilities, education, religion, gender identity, sexual orientation, and more. USP provides comprehensive benefits to ensure the personal and financial wellbeing of its employees, including company-paid time off, healthcare options, and retirement savings. (Note: USP does not accept unsolicited resumes from 3rd party recruitment agencies.) **Role Overview:** As a ML Engineer at U.S. Pharmacopeial Convention (USP), you will be a crucial part of the Digital product engineering team. Your expertise in building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities will play a key role in supporting projects aligned with USP's mission to protect patient safety and improve global health. You will be responsible for implementing scalable data collection, storage, and processing ML pipelines, optimizing data models and pipelines to support self-service analytics tools, and collaborating with data scientists to operationalize machine learning models. **Key Responsibilities:** - Implement scalable data collection, storage, and processing ML pipelines to support enterprise-wide data needs. - Build and optimize data models and pipelines for self-service analytics tools like Tableau, Looker, and Power BI. - Collaborate with data scientists to integrate machine learning models into production data pipelines for scalability and performance. - Build and maintain data pipelines for structured, semi-structured, and unstructured data. - Support ETL/ELT workflows and data ingestion using batch, streaming, and API-based approaches. - Develop and optimize data processing jobs using Python and distributed frameworks. - Participate in building, training, and deploying machine learning models and data-driven applications. - Create dashboards and visualizations for analytics and business reporting. - Assist in deploying data and ML workloads to cloud environments following best practices. - Collaborate with senior engineers and cross-functional teams to understand requirements and deliver solutions. - Write clean, maintainable, and well-documented code, and participate in code reviews. - Continuously learn and apply best practices in data engineering, ML engineering, and MLOps. **Qualifications Required:** - Bachelors degree in a relevant field (e.g. E