Apree Health is a leading innovator in the healthcare technology space, dedicated to improving patient outcomes and revolutionizing the way healthcare is delivered. We leverage cutting-edge machine learning and artificial intelligence to develop solutions that empower healthcare providers, enhance patient experiences, and drive efficiency across the healthcare ecosystem.
Job Summary:
As a Machine Learning Engineer at Apree Health, you will be instrumental in building and maintaining the robust data infrastructure that fuels our machine learning initiatives. You will work at the intersection of data engineering and machine learning, ensuring high-quality, reliable data is available to power our models and drive insights. A deep understanding of healthcare data, including Electronic Medical Records (EMR) and claims data, is essential for this role.
Key Responsibilities:
- Data Engineering:
- Design, build, and maintain robust data pipelines to collect, process, and store large volumes of healthcare data from various sources.
- Ensure data quality, integrity, and security throughout the data lifecycle, implementing data validation and cleaning processes.
- Implement data warehousing and data lake solutions to support data exploration, analysis, and machine learning model development.
- Optimize data infrastructure for performance, scalability, and cost-efficiency.
- Collaborate with data scientists to understand their data needs and provide them with the necessary data access and tools.
- Machine Learning:
- Work closely with data scientists to prepare data for machine learning model development, including feature engineering and selection.
- Assist in model deployment and monitoring, ensuring models are integrated seamlessly into the data pipeline.
- Contribute to the development and optimization of machine learning models, leveraging your understanding of the underlying data.
- Healthcare Data Expertise:
- Demonstrate expertise in working with healthcare data, including EMR and claims data, understanding their structures, terminologies, and complexities.
- Extract, transform, and load (ETL) data from EMR and claims systems, ensuring data is standardized and ready for analysis.
- Apply domain knowledge to identify relevant features and patterns in healthcare data to improve machine learning model performance.
Qualifications:
- Master's degree or PhD in Computer Science, Machine Learning, Statistics, or a related field.
- 4+ years of hands-on experience in data engineering and building data pipelines, focusing on healthcare data, including EMR and claims data.
- Experience with machine learning model development and deployment is a plus.
Technical Skills:
- Strong proficiency in Python and SQL.
- Expertise in data pipeline tools (e.g., Airflow, Data Fusion) and big data technologies (e.g., BigQuery).
- Familiarity with GCP and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn) is highly desirable.
- Knowledge of healthcare data standards (e.g., HL7, FHIR) and terminologies (e.g., ICD-10, SNOMED CT) is a strong plus.
Additional Skills:
- Exceptional analytical and problem-solving skills, with the ability to tackle complex data challenges.
- Excellent written and verbal communication skills, with the ability to explain technical concepts to both technical and non-technical audiences.
- A strong passion for leveraging data and machine learning to make a positive impact on healthcare.
Compensation: $185k-$221k/annual salary & bonus eligible
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