Lead Machine Learning Engineer, Digital Products

Description

Who is USP?

The U.S. Pharmacopeial Convention (USP) USP is an independent scientific organization that collaborates with the world's top experts in health and science to develop quality standards for medicines, dietary supplements, and food ingredients. USP's fundamental belief that Equity = Excellence manifests in our core value of Passion for Quality through our more than 1,100 talented professionals across five global locations to deliver the mission to strengthen the supply of safe, quality medicines and supplements worldwide.

Brief Job Overview

The Digital product engineering team at USP is seeking a Lead ML Engineer with expertise in building robust Data/ML pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities. This role is critical to supporting projects that align with our mission to protect patient safety and improve the health of people around the world. We are looking for a professional who understands the value of clean, accessible, and well-organized data, and who enjoys designing systems that empower data scientists, analysts, and business stakeholders to derive actionable insights. The ideal candidate will be passionate about data architecture, cloud technologies, and scalable engineering solutions that drive innovation and impact.

How will YOU create impact here at USP?

The position's purpose should provide a high-level overview of why the position exists and briefly identify the most critical priorities of the position. This is an opportunity to highlight any features or duties of the role related explicitly to the Diversity, Equity, Inclusion & Belonging work of the Department.

• Design and implement scalable data collection, storage, and processing ML pipelines to support enterprise-wide data needs.
• Implement and maintain data governance frameworks and data quality checks within data pipelines to ensure compliance and reliability.
• Build and optimize data models and data pipelines to support self-service analytics and reporting tools such as Tableau, Looker, and Power BI.
• Collaborate with data scientists to operationalize machine learning models by integrating them into production data pipelines and ensuring scalability and performance.
• Develop and manage ETL/ELT workflows and orchestration using tools like Airflow or AWS Step Functions to ensure efficient data movement and transformation.
• Implement CI/CD practices for ML models and data pipelines, including automated testing, containerization, and deployment

Who USP is Looking For?

The successful candidate will have a demonstrated understanding of our mission, commitment to excellence through inclusive and equitable behaviors and practices, ability to quickly build credibility with stakeholders, along with the following competencies and experience:

Education

Bachelor’s degree in relevant field (e.g. Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience.

Experience

• 7+ years of experience designing, building, and optimizing large-scale data platforms and pipelines for structured, semi-structured, and unstructured data.
• Expert in ETL/ELT workflows, data ingestion (streaming, batch, APIs), and distributed processing using Apache Spark, PySpark, Airflow, Glue, and modern orchestration frameworks.
• Strong experience architecting and integrating data across heterogeneous systems, including data lakes and warehouses (AWS S3, Redshift, Snowflake, Delta Lake).
• Deep knowledge of data quality frameworks, data governance, metadata management, and SQL optimization for analytical workloads.
• Advanced Python/PySpark skills with hands-on experience in data processing and API development (FastAPI, Flask, Django).
• Deep expertise in AWS services including S3, RDS, Redshift, Lambda, Step Functions, SageMaker, EC2/ECR, CloudWatch, ALB/NLB, and autoscaling.
• Strong foundation in ML system design, feature stores, model registries, A/B testing, and deploying ML models with high availability and autoscaling.
• Knowledge of GenAI/LLM workloads including RAG pipelines, vector stores, chunking, prompt engineering, and embeddings, is a plus
• Skilled in containerization and orchestration using Docker and Kubernetes for scalable data and ML deployments.
• Experience with CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and automated deployments.
• Strong collaboration skills with cross-functional teams including product, architecture, engineering, and business stakeholders.

Additional Desired Preferences

• Experience with scientific chemistry nomenclature or prior work experience in life sciences, chemistry, or hard sciences or degree in sciences
• Experience with pharmaceutical datasets and nomenclature
• Experience in developing Machine Learning & Deep Learning models
• Ability to explain complex technical issues to a non-technical audience
• Strong communication skills required: Verbal, written, and interpersonal

Supervisory Responsibilities:

No

Benefits

USP provides you with the benefits you need to protect yourself and your family today and tomorrow. From company-paid time off, comprehensive healthcare options to retirement savings, you can have peace of mind that your personal and financial wellbeing is protected.

USP is proud to be an equal employment opportunity employer (EEOE) and affirmative action employer. We are committed to creating an inclusive environment in all aspects of our work—from the standards we make to the partnerships and conversations we cultivate. An environment where every employee feels fully empowered and valued irrespective of, but not limited to, personality, race, ethnicity, physical and mental abilities, education, religion, gender identity and expression, life experience, sexual orientation, country of origin, regional differences, work experience, and family status. We are committed to working with and providing reasonable accommodation to individuals with disabilities.

Note: USP does not accept unsolicited resumes from 3rd party recruitment agencies and is not responsible for fees from recruiters or other agencies except under specific written agreement with USP.

Job Category Information Technology

Job Type Full-Time