Data Scientist (Healthcare)100% Remote

Data Scientist (Healthcare) 100% Remote

What you will do:

The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse.

• Experience with machine learning and statistical analyses are needed.
• Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.
• Develop and implement algorithms on existing data warehouse records and identify new external data sources to be ingested to the data warehouse to strengthen analyses.
• Analysis will address a wide variety of clinical and research outcomes.
• Research and implement AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data.
• The successful candidate will have demonstrated competence in developing highly scalable artificial intelligence systems with multiple dependencies across teams.

What gets you the job:

Programming Languages

• Python (for preprocessing, data analysis, machine learning, scripting)
• SQL (for database querying and management)
• SAS (common in healthcare data analysis)
• MATLAB (for algorithm development, though less common in healthcare)
• R (for statistical computing and bioinformatics)

Data Science & Machine Learning Frameworks

• TensorFlow, PyTorch, Keras (for deep learning and complex machine learning, including neural networks and advanced AI)
• Scikit-Learn (for classical machine learning)
• XGBoost or LightGBM (for gradient boosting in structured data)
• Large Language Models (LLMs) (for text generation, summarization, etc.)
• AWS SageMaker (for end-to-end machine learning development, training and scalable machine learning in a managed cloud enviornment)
• Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data)
• AWS Bedrock (for accessing pre-trained LLMs and foundation models without managing infrastructure)

Healthcare-Specific Knowledge

• HL7 (Health Level Seven International standards for electronic health information exchange)
• FHIR (Fast Healthcare Interoperability Resources standard for exchanging healthcare information electronically)
• ICD-10 Coding (for medical diagnosis and procedure classification)
• HIPAA Compliance (handling sensitive patient data securely)
• Clinical Terminologies (e.g., SNOMED, LOINC)

Data Tools & Platforms

• SQL-based Databases (e.g., PostgreSQL, MySQL, Microsoft SQL Server)
• Data Warehousing (e.g., AWS Redshift, Google BigQuery)
• Data Visualization Tools (e.g., Tableau, Power BI, Plotly)
• NoSQL Databases (e.g., MongoDB, Cassandra)
• Apache Hadoop or Apache Spark (for big data processing)
• ETL Tools (e.g., Informatica, Talend, AWS Glue)

Statistical & Analytical Techniques

• Regression Analysis (linear, logistic, multinomial, ordinal, etc.)
• Descriptive Statistical tests (correlaton, covariance, chi-square, univariate, multivariate analyses)
• Clustering Techniques (e.g., k-means, hierarchical clustering etc)
• Dimensionality Reduction (e.g., Principal Component Analysis (PCA),
• Natural Language Processing (NLP) (for analyzing clinical notes or electronic health records)
• Predictive Modeling (for patient outcomes, risk analysis)
• Time Series Analysis (useful for patient monitoring, trend analysis)
• Survival Analysis (for patient outcome predictions)
• A/B Testing (for clinical trials or health interventions)

Cloud Computing & DevOps Skills

• AWS, Google Cloud, or Azure (cloud platforms for scalable computing)
• AWS Lambda and Step Functions (serverless computing and workflow automation)
• Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility)
• CI/CD Pipelines (for automating deployment and monitoring of machine learning models)

Electronic Health Records (EHR) Systems

• Experience with Epic or Cerner (popular EHR systems in healthcare)

Data Governance & Security

• Data Privacy (understanding of privacy laws such as HIPAA, GDPR)
• Data Anonymization or De-identification techniques (for research and compliance)
• Auditing & Compliance Tools (for ensuring secure and compliant data handling)
• Responsible AI Practices (AI governance frameworks ensuring healthcare regulations and ethical standards)

\Bachelors Degree computer science, artificial intelligence, informatics or closely related field

Masters preferred