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Anurag Patil
Mid-level Data Analyst specializing in machine learning, ETL, and real-world evidence analytics
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About
Developed and productionized an AI-driven "indication finding" system for AbbVie to identify additional diseases a drug could target, working closely with clinical research teams on cohort inclusion/exclusion criteria and disease rollups. Leveraged an LLM to map clinical inputs to ICD codes and built configuration-driven ML pipelines (Cloudera ML, YAML, scheduled jobs) with structured testing and evaluation for reliability.
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Key Strengths
- Built and deployed an AI system for drug indication finding in a biopharma context
- Strong cross-functional collaboration with clinical research stakeholders to refine cohort criteria
- Used an LLM to translate clinical stakeholder inputs into ICD code mappings to streamline cohort parameter updates
- Structured, configuration-driven approach to ML pipeline execution (YAML + modular stages)
- Methodical approach to embedding representation and hyperparameter tuning via pipeline-integrated parameter files
- Emphasis on reliability/traceability through multi-level testing (unit, data validation, scenario-based) and measurable metrics
- Able to explain complex ML concepts (embedding space/weighting) to non-technical stakeholders using analogies
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