Senior Data Scientist

Location: Remote / Hybrid (HQ: [City])

Company: OpenNetworks.org

Compensation: $155,000 – $185,000 + Significant Founding-Level Equity

The Mission: Rewire the System

OpenNetworks.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 Intelligence

• Automated 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.