Data Scientist II
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Before you apply
LocationMiami
Typefull-time
SeniorityMid
Experience3–5 yrs
Company size1,001–5,000 people
First seenOct 18, 2026 · 2w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in data science, Statistics, Computer Science, Mathematics, Biostatistics, Bioinformatics, or related quantitative field
3-5 years of experience in data science with demonstrated experience in healthcare or life sciences environments
Experience working with healthcare data, including electronic health records (EHR), clinical databases and/or ERP data
Proven track record of applying statistical analysis and machine learning techniques to real-world healthcare and healthcare operational problems
Proficiency in programming languages such as Python, R, and SQL for data analysis and modeling
Strong knowledge of statistical modeling, machine learning, and data visualization techniques
Master's degree in related quantitative fields
Familiarity with LLMs and foundation models for natural language processing and clinical data analytics
Familiarity with graph data platforms (e.g., Neo4j, Amazon Neptune) and vector databases (e.g., Pinecone)
Experience with precision medicine initiatives, genomics data, or personalized healthcare analytics
Experience with distributed computing, big data frameworks, and cloud platforms (e.g., Azure, AWS, GCP)
Familiarity with healthcare information systems and EHR platforms
Experience with data integration from multiple healthcare sources
Understanding of healthcare regulations including HIPAA, FDA guidelines, and clinical research compliance requirements
Familiarity with medical coding systems (ICD-10, CPT, SNOMED)
Excellent written and verbal communication skills with ability to present complex analytical findings clearly to technical and non-technical audience
Ability to work effectively in interdisciplinary teams with clinicians, researchers, and IT professionals
Skilled at translating business requirements into technical solutions
Strong attention to detail and commitment to data accuracy and integrity
Ability to manage multiple projects simultaneously and meet deadlines
Ability to work independently while collaborating effectively with team members
Skills
Python
R
SQL
Neo4j
Amazon Neptune
Pinecone
Azure
AWS
GCP
ICD-10
CPT
SNOMED
Job Summary
Responsible for leveraging advanced analytics, machine learning and statistical methodologies to extract meaningful insights from healthcare and operational data at Nicklaus Children’s Health System. Areas of focus include analysis of electronic health records (EHR), genomic and precision medicine initiatives, financials and enterprise resource planning (ERP) data. Collaborates with researchers, clinical and operational teams within the health system. Works to ensure all data analysis activities are performed in accordance with applicable federal regulations, HIPAA requirements, and health system policies and procedures.
Job Specific Duties
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Applies machine learning, natural language processing, and AI techniques to analyze structured and unstructured healthcare data, including clinical, genomics, and operational datasets.
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Develops predictive models integrating multi-omics data (genomics, proteomics), biomarkers, and clinical outcomes to support precision medicine and personalized care pathways.
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Builds scalable data pipelines and connects complex healthcare data using graph database technologies (e.g., property graphs) to uncover hidden relationships across patients, treatments, and outcomes.
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Collaborates with data engineering and IT teams to operationalize models for real-time decision support, risk stratification, and resource optimization across clinical and business operations.
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Develops and validates predictive models to support clinical decision support, risk stratification, and outcome prediction for pediatric patient populations.
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Ensures all analytical activities comply with healthcare regulations, institutional policies, and research ethics requirements, including proper handling of protected health information.
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Partners with clinicians, researchers, and administrators to understand analytical needs, translate requirements into technical solutions, and communicate effectively.
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Maintains comprehensive documentation of analytical methods, validate analytical approaches, and ensure reproducibility of research findings.
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Provides technical expertise and training team members on data analysis techniques and healthcare analytics best practices.
Minimum Job Requirements
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Bachelor’s degree in data science, Statistics, Computer Science, Mathematics, Biostatistics, Bioinformatics, ore related quantitative field. (Required)
•
3-5 years years of experience in data science with demonstrated experience in healthcare or life sciences environments.
•
Experience working with healthcare data, including electronic health records (EHR), clinical databases and/or ERP data.
•
Proven track record of applying statistical analysis and machine learning techniques to real-world healthcare and healthcare operational problems.
Knowledge, Skills, and Abilities
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Master’s degree in related quantitative fields is preferred.
•
Proficiency in programming languages such as Python, R, and SQL for data analysis and modeling is required.
•
Strong knowledge of statistical modeling, machine learning, and data visualization techniques.
•
Familiarity with LLMs and foundation models for natural language processing and clinical data analytics.
•
Familiarity with graph data platforms (e.g., Neo4j, Amazon Neptune) and vector databases (e.g., Pinecone). Experience with precision medicine initiatives, genomics data, or personalized healthcare analytics.
•
Experience with distributed computing, big data frameworks, and cloud platforms (e.g., Azure, AWS, GCP).
•
Familiarity with healthcare information systems and EHR platforms.
•
Experience with data integration from multiple healthcare sources.
•
Understanding of healthcare regulations including HIPAA, FDA guidelines, and clinical research compliance requirements.
•
Familiarity with medical coding systems (ICD-10, CPT, SNOMED).
•
Excellent written and verbal communication skills with ability to present complex analytical findings clearly to technical and non-technical audience.
•
Ability to work effectively in interdisciplinary teams with clinicians, researchers, and IT professionals.
•
Skilled at translating business requirements into technical solutions.
•
Strong attention to detail and commitment to data accuracy and integrity.
•
Ability to manage multiple projects simultaneously and meet deadlines.
•
Ability to work independently while collaborating effectively with team members.