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Principal Machine Learning Engineer

1 month ago

Elmwood Park, Illinois, United States Abbott Full time
About Abbott

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines.

Our Mission

We are committed to delivering innovative solutions that improve the quality of life for millions of people around the world. Our medical devices, with revenue of over $14 billion in 2021, is a global leader and help more than 10,000 people have healthier hearts, improve the quality of life for thousands of people living with chronic pain and movement disorders, and liberate more than 500,000 people with diabetes from routine fingersticks.

What We Offer
  • Career development with an international company where you can grow the career you dream of.
  • Free medical coverage for employees via the Health Investment Plan (HIP) PPO.
  • An excellent retirement savings plan with high employer contribution.
  • Tuition reimbursement, the Freedom 2 Save student debt program and FreeU education benefit - an affordable and convenient path to getting a bachelor's degree.
  • A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune.
  • A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.
The Opportunity

This Principal Machine Learning Engineer position works out of the Chicago, IL office in the Medical Devices Digital Solutions organization. The Principal Machine Learning Engineer role will work closely with cross-functional product teams and be responsible for designing, developing, and deploying state-of-the-art data engineering techniques and streamlined data ingestion processes to extract valuable insights and intelligence from large and complex medical datasets (structured and unstructured data).

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 plus 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) for data preparation and analysis.
  • Experience with Data Ops (VPCs, cluster management, permissions, Databricks configurations, Terraform) in Cloud Computing environments (e.g., AWS, Azure, GCP) and associated cloud data platforms, cloud data warehouse technologies (Snowflake/Redshift), and Advanced Analytical platforms (e.g., Dataiku and Databricks).
  • 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 to convey complex technical concepts and findings to non-technical stakeholders and collaborate effectively across teams.
Preferred Qualifications
  • 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 - RDF, RDFS, OWL, SKOS, SPARQL and associated Ontology Editors (e.g., TopBraid Composer, Protégé).
  • Experience with semantic validation languages (e.g. SHACL, SPIN) and associated semantic software packages and frameworks (e.g., Jena, Sesame, RDF4J, RDFLib).
  • Knowledge of data governance and compliance policies.