Senior Machine Learning Engineer, Digital Products

Role Overview: As a Senior ML Engineer at the U.S. Pharmacopeial Convention (USP), you will be a crucial part of the Digital product engineering team, responsible for building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities. Your role is essential in supporting projects that aim to protect patient safety and enhance global health. You will play a key role in designing systems that facilitate actionable insights for data scientists, analysts, and business stakeholders. Your passion for data architecture, cloud technologies, and scalable engineering solutions will drive innovation and impact within the organization. Key Responsibilities: - Design and implement scalable data collection, storage, and processing ML pipelines to meet enterprise-wide data requirements. - Establish and maintain data governance frameworks and data quality checks in data pipelines to ensure compliance and reliability. - Develop and optimize data models and pipelines to support self-service analytics and reporting tools like Tableau, Looker, and Power BI. - Collaborate with data scientists to operationalize machine learning models by integrating them into production pipelines and ensuring scalability and performance. - Create and manage ETL/ELT workflows and orchestration using tools such as Airflow or AWS Step Functions for efficient data movement and transformation. - Implement CI/CD practices for ML models and data pipelines, including automated testing, containerization, and deployment. Qualifications Required: - Bachelors degree in a relevant field (e.g., Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience. - Minimum of 3 years of experience in designing, building, and optimizing large-scale data platforms and pipelines for structured, semi-structured, and unstructured data. - Expertise in ETL/ELT workflows, data ingestion, and distributed processing using tools like Apache Spark, PySpark, Airflow, Glue, etc. - Strong knowledge of architecting and integrating data across heterogeneous systems, including data lakes and warehouses (AWS S3, Redshift, Snowflake, Delta Lake). - Proficiency in data quality frameworks, data governance, metadata management, and SQL optimization for analytical workloads. - Advanced skills in Python/PySpark with hands-on experience in data processing and API development. - Deep expertise in AWS services and ML system design, including feature stores, model registries, and deploying ML models with high availability. - Experience in containerization and orchestration using Docker and Kubernetes for scalable data and ML deployments. - Familiarity with CI/CD pipelines, infrastructure-as-code, and automated deployments. - Excellent collaboration skills with cross-functional teams including product, architecture, engineering, and business stakeholders. Additional Details: USP is an equal employment opportunity employer committed to creating an inclusive environment. They provide comprehensive benefits to ensure the personal and financial well-being of their employees. USP does not accept unsolicited resumes from 3rd party recruitment agencies. (Note: Supervisory responsibilities are not applicable for this role) Role Overview: As a Senior ML Engineer at the U.S. Pharmacopeial Convention (USP), you will be a crucial part of the Digital product engineering team, responsible for building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities. Your role is essential in supporting projects that aim to protect patient safety and enhance global health. You will play a key role in designing systems that facilitate actionable insights for data scientists, analysts, and business stakeholders. Your passion for data architecture, cloud technologies, and scalable engineering solutions will drive innovation and impact within the organization. Key Responsibilities: - Design and implement scalable data collection, storage, and processing ML pipelines to meet enterprise-wide data requirements. - Establish and maintain data governance frameworks and data quality checks in data pipelines to ensure compliance and reliability. - Develop and optimize data models and pipelines to support self-service analytics and reporting tools like Tableau, Looker, and Power BI. - Collaborate with data scientists to operationalize machine learning models by integrating them into production pipelines and ensuring scalability and performance. - Create and manage ETL/ELT workflows and orchestration using tools such as Airflow or AWS Step Functions for efficient data movement and transformation. - Implement CI/CD practices for ML models and data pipelines, including automated testing, containerization, and deployment. Qualifications Required: - Bachelors degree in a relevant field (e.g., Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience.