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Senior Data Scientist
JOB SUMMARY
Roles
Job details
Senior Data Scientist
Location
: Remote / Hybrid (HQ: [City])Company: OpenNetworks. orgCompensation: $155,000 – $185,000 + Significant Founding-Level EquityThe Mission: Rewire the SystemOpenNetworks. org is not another "health plan. " We are the open-source infrastructure for a transparent healthcare marketplace. While the rest of the industry hides behind "black box" pricing and gag clauses, we are leveraging transparency laws to connect purchasers and providers directly.
As a Founding Data Scientist, you won't be a "SQL Monkey. " You will architect the intelligence that makes transparent pricing possible at scale.
You will be the first Data Scientist at OpenNetworks.
The Role: Architecting IntelligenceAutomated Repricing Engines: Build and deploy real-time classification models to ingest contracts, claims, Price Transparency, employer and employee data to classify and create insights. Agentic ML Pipelines: Move beyond "insights" to "actions. " Design agents that identify network gaps and automatically curate provider lists for self-funded employers. Deep Classification: Engineer sophisticated multi-class models to categorize provider specializations, clinical risk tiers, and longitudinal claims records. GenAI/NLP Integration: Implement "Ambient Intelligence" to extract structured pricing and clinical intent from unstructured PDF contracts and clinical notes. Rapid Production: We operate in weeks, not quarters.
You will own the full lifecycle—from hypothesis in a notebook to a production-grade microservice. Required
Qualifications
Experience: 4+ years in Data Science.
You have ideally made significant contributions before in a HealthTech startup. Technical Stack: Mastery of Python, SQL, and the 2026 ML stack (PyTorch, XGBoost, LangChain/AutoGPT for agentic workflows). Cloud Containerization: Strong experience with a major cloud provider (AWS), DataOcean vendors such as Snowflake, DataBricks, and/or SageMaker. MLOps Tools: Hands-on experience with MLOps frameworks and platforms (e. g. , MLflow, Kubeflow, Sagemaker, TFX, or similar). Healthcare Fluency: You know your way around NPIs, CPT codes, ICD-10, and FHIR standards.
You understand why healthcare data is "dirty" and how to clean it without losing context. Mathematical Rigor: Understanding of classification metrics (F1-score, Precision-Recall curves) in the context of imbalanced healthcare datasets. Technical Foundation: Deep understanding of the machine learning lifecycle, from data prep and model training to deployment and monitoring. Preferred
Qualifications
Experience working in the HealthTech or FinTech industries, particularly with highly regulated data. Experience designing and managing data pipelines (ETL/ELT) for ML features. Knowledge of data governance, security principles, and compliance
requirements
in healthcare (e. g. , HIPAA). Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
Location
: Remote / Hybrid (HQ: [City])Company: OpenNetworks. orgCompensation: $155,000 – $185,000 + Significant Founding-Level EquityThe Mission: Rewire the SystemOpenNetworks. org is not another "health plan. " We are the open-source infrastructure for a transparent healthcare marketplace. While the rest of the industry hides behind "black box" pricing and gag clauses, we are leveraging transparency laws to connect purchasers and providers directly.
As a Founding Data Scientist, you won't be a "SQL Monkey. " You will architect the intelligence that makes transparent pricing possible at scale.
You will be the first Data Scientist at OpenNetworks.
The Role: Architecting IntelligenceAutomated Repricing Engines: Build and deploy real-time classification models to ingest contracts, claims, Price Transparency, employer and employee data to classify and create insights. Agentic ML Pipelines: Move beyond "insights" to "actions. " Design agents that identify network gaps and automatically curate provider lists for self-funded employers. Deep Classification: Engineer sophisticated multi-class models to categorize provider specializations, clinical risk tiers, and longitudinal claims records. GenAI/NLP Integration: Implement "Ambient Intelligence" to extract structured pricing and clinical intent from unstructured PDF contracts and clinical notes. Rapid Production: We operate in weeks, not quarters.
You will own the full lifecycle—from hypothesis in a notebook to a production-grade microservice. Required
Qualifications
Experience: 4+ years in Data Science.
You have ideally made significant contributions before in a HealthTech startup. Technical Stack: Mastery of Python, SQL, and the 2026 ML stack (PyTorch, XGBoost, LangChain/AutoGPT for agentic workflows). Cloud Containerization: Strong experience with a major cloud provider (AWS), DataOcean vendors such as Snowflake, DataBricks, and/or SageMaker. MLOps Tools: Hands-on experience with MLOps frameworks and platforms (e. g. , MLflow, Kubeflow, Sagemaker, TFX, or similar). Healthcare Fluency: You know your way around NPIs, CPT codes, ICD-10, and FHIR standards.
You understand why healthcare data is "dirty" and how to clean it without losing context. Mathematical Rigor: Understanding of classification metrics (F1-score, Precision-Recall curves) in the context of imbalanced healthcare datasets. Technical Foundation: Deep understanding of the machine learning lifecycle, from data prep and model training to deployment and monitoring. Preferred
Qualifications
Experience working in the HealthTech or FinTech industries, particularly with highly regulated data. Experience designing and managing data pipelines (ETL/ELT) for ML features. Knowledge of data governance, security principles, and compliance
requirements
in healthcare (e. g. , HIPAA). Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
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