Technical
Skills & Technologies
Languages
& Query
•
SQL- data profiling, validation rules, quality checks, large-scale dataset
analysis
• Python - automation, ML/AI
techniques, scripting, pipeline development
Data Quality & Governance
• Data quality frameworks
(Great Expectations, dbt tests) augmented with ML/LLM
• Data governance practices
(metadata management, lineage review, issue tracking)
• Data stewardship,
remediation workflows, and automated quality monitoring pipelines
• Data lineage and impact
analysis
• Data completeness and
reliability measurement
ML/AI for Data Quality
• Supervised learning -
classification models to flag bad records (e.g., mislabeled diagnosis codes,
incorrect attributions)
• Unsupervised learning -
clustering to detect outliers, unusual patterns, and grouping anomalies in
clinical data
• Anomaly detection -
model-driven (Isolation Forest, LOF, One-Class SVM)
• Data drift monitoring -
statistical process control (KS test, PSI, Jensen-Shannon divergence)
• NLP / Text Analytics -
parsing unstructured clinical notes, extracting entities, detecting coding
inconsistencies
• Record linkage / Entity
resolution - ML-based deduplication using probabilistic matching (e.g., patient
matching across systems)
• Pattern recognition and
root cause analysis
AI Frameworks & Libraries
• scikit-learn - anomaly
detection, clustering, classification
• PyOD - outlier detection
• TensorFlow / PyTorch - deep
learning for complex pattern recognition in large clinical datasets
• LLMs (GPT, Claude, etc.) -
automated root cause summarization, natural language quality reports, code
review assistance
Dash boarding & Reporting
• Alerting and monitoring
dashboards (Tableau, Power BI, Streamlit, or similar)
• Data quality trend reporting and
operational risk communication
Healthcare Domain Knowledge
• Healthcare coding systems (ICD,
CPT, HCPCS)
• Member attribution logic
• Clinical quality measures
(HEDIS, Stars)
• Interoperability data (HL7,
FHIR)
•
Payer/provider/clinical/population health datasets
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