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# Senior Data Scientist

- **Company:** Spirite Industries, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Big Data, BigQuery, Continuous Integration, Data Deduplication, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Neo4j, SQL Databases, Snowflake, Git, Fastapi, Pytest, Docker, Databricks
- **Published:** September 21, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/plrdixomkg

## About the Role

* Minimum 5 years of experience in shipping production ML or data-science work, including hands-on entity linking and resolution at scale
 * Deep expertise in Python, including production-grade, tested, and type-hinted code
 * Strong command of SQL and experience with FastAPI, Docker, pytest, Git, and CI/CD practices
 * Practical experience in probabilistic methods for match scoring, threshold selection, and evaluation
 * Comfort with graph algorithms and graph ML techniques, including embeddings and GNNs
 * Experience with data and ML engineering, including ETL processes over large datasets and data-quality monitoring
 * Familiarity with distributed SQL warehouses such as Snowflake, BigQuery, or Databricks, and graph technologies like Neo4j or Amazon Neptune
 * Advanced level of English

## Description

* Extracting entities and attributes from heterogeneous sources including warehouse tables, registries, and documents
 * Designing and implementing probabilistic entity linking and deduplication across multiple large healthcare datasets
 * Owning the linking pipeline from end to end, including pairwise and graph-based scoring and calibrated match probabilities
 * Defining how link quality is measured through precision, recall, calibration, and error analysis
 * Linking entities and attributes to controlled medical vocabularies to ensure consistency across sources
 * Utilizing graph representations and graph ML techniques for linking, including clustering, centrality, and link prediction
 * Building and maintaining the data and graph estate, including warehouse transformation and monitoring
 * Improving the performance of existing algorithms in response to changing data sources and volumes


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