PRINCIPAL MACHINE LEARNING ENGINEER

$109,300 - $218,700/Yr

Abbott Laboratories - Chicago, IL

Full-time - Senior

Chicago, IL

10,001+ employees

Miscellaneous Manufacturing

About the position

The Principal Machine Learning Engineer at Abbott will play a crucial role in the Medical Devices Digital Solutions organization, focusing on designing, developing, and deploying advanced data engineering techniques to extract insights from complex medical datasets. This position involves collaboration with cross-functional teams to enhance patient care through innovative medical devices and therapy solutions.

Responsibilities

• Lead and contribute hands-on to major data engineering initiatives from inception to delivery.
• Analyze data to identify trends and insights.
• Collaborate with product and engineering teams to define data requirements and drive data-driven decision-making.
• Design and implement data models to effectively support various product use cases.
• Design, implement, and maintain scalable and optimized data architectures that meet evolving business needs.
• Evaluate and recommend appropriate data storage solutions, ensuring data accessibility and integrity.
• Develop and continuously optimize data ingestion processes for improved reliability and performance.
• Design, build, and maintain robust data pipelines and platforms.
• Establish monitoring and alerting systems to proactively identify and address potential data pipeline issues.
• Support Data infrastructure needs such as cluster management and permission.
• Develop and maintain internal tools to streamline data access and analysis for all teams.
• Create and deliver documentation to educate product teams on data best practices and tools.
• Communicate technical concepts effectively to both technical and non-technical audiences.

Requirements

• Master's Degree in Data Science, Computer Science, Statistics, or a related field.
• 10 years of experience in data engineering with a strong focus on data architecture and data ingestion.
• Experience in the Life Science Industry.
• Strong understanding of data modeling (conceptual, logical, and physical) using different data modeling methodologies and analytics concepts.
• Proven experience designing, building, and maintaining data pipelines and platforms.
• Expertise in data integration, ETL tools, and data engineering programming/scripting languages (Python, Scala, SQL).
• Experience with Data Ops in Cloud Computing environments (e.g., AWS, Azure, GCP) and associated cloud data platforms.
• Familiarity with data streaming technologies like Kafka and Debezium.
• Proven expertise with data visualization tools (e.g., Tableau, Power BI).
• Strong understanding of data security principles and best practices.
• Experience with CI/CD pipelines and automation tools.
• Strong problem-solving and critical thinking skills.
• Excellent written and verbal communication skills.

Nice-to-haves

• Prior experience with healthcare domain data, including Electronic Health Records (EHR).
• Experience with triple stores or graph databases (e.g., GraphDB, Stardog, Jena Fuseki).
• Proficient with building domain ontologies and relevant W3C standards.
• Experience with semantic validation languages and associated semantic software packages and frameworks.
• Knowledge of data governance and compliance policies.

Benefits

• Free medical coverage for employees via the Health Investment Plan (HIP) PPO.
• Excellent retirement savings plan with high employer contribution.
• Tuition reimbursement and education benefits.
• Career development opportunities with an international company.