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JYOTHI N
Senior Data Scientist specializing in analytics, experimentation, and BI on AWS
Austin, TXData Scientist / Business Intelligence Engineer7 years experienceSeniorHealthcareHealthcare ITE-commerce
ScreenedReferences VerifiedIdentity VerifiedStrongly Recommended
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About
Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.
Experience
Data Scientist / Business Intelligence EngineerAmazon
Data ScientistLoma Linda University Health
Data ScientistStee Software
Education
JNTU Universitybachelor, Information Technology
Key Strengths
- Improved healthcare data quality on 10M+ records; reduced data errors by 40% and delivered $500k annual savings
- Built scalable AWS-based ETL and data validation pipelines (Glue/Redshift/Lambda) with automation
- Strong entity resolution for messy, unstructured data using fuzzy matching and embedding-based similarity
- Validated matching/model performance with precision/recall, sampling, and statistical tests (t-test, chi-square)
- Production-grade Python workflow practices: modular pipelines, orchestration (Airflow/Glue), testing (pytest), data validation (Great Expectations), monitoring (CloudWatch), Docker, CI/CD
- Fine-tuned domain-specific embedding models to improve healthcare matching accuracy
Reference Highlights
Strongly Recommended
- Strong ability to design and implement ML/NLP solutions at scale (9/10)
- Excellent work on provider effectiveness and length-of-stay analysis using ML
- Strong Python skills
- Writes clean, maintainable code
- Builds reliable, scalable data pipelines
- Emphasis on validation and monitoring
- Clear communication with technical and non-technical stakeholders
- Effective cross-functional collaboration
- Bridges business and engineering effectively
- Clarifies definitions and aligns stakeholders quickly
- Documents agreed logic clearly
- Full problem ownership from investigation through delivery
- Makes informed decisions based on data, impact, and feasibility
- Delivers reliable, scalable, production-ready solutions
- Strong testing and documentation practices
- Hardworking
- Teamwork
- Effective and smart work
- Reliable
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