Note: The job is a remote job and is open to candidates in USA. Brundage Group provides mid-cycle clinical revenue cycle support and software solutions that help hospitals remain financially viable. The Data Science Engineer will build, evaluate, deploy, and monitor machine learning models and LLM systems for utilization management, denial-risk scoring, and related clinical workflows, partnering with Physician Advisors, IT, and product engineering.
Responsibilities
- Train, evaluate, and deploy models for case prioritization, level-of-care and status-determination support, and denial-risk scoring. Own each one from problem framing through production monitoring
- Design retrieval systems over clinical notes and payer policy documents. Build summarization for peer-to-peer prep and drafting support for appeals. Version your prompts, log your outputs, and build the guardrails
- Build labeled gold sets with Physician Advisors. Publish accuracy, precision, recall, and the error profile for every model. Run the evaluation harness in CI so nothing ships without passing the gate
- Build feature pipelines from the Certus Connect FHIR feed and Salesforce case data. Handle messy clinical data, missing fields, and inconsistent coding. Keep every transform traceable
- Ship an explanation with every score. Design for HIPAA and HITRUST from the start, not as a review at the end. Flag PHI risk before anyone asks
- Monitor drift, track accuracy on a rolling sample, set the retraining triggers, and write the runbook. Catch problems before a client does
- Partner with Physician Advisors on clinical ground truth, IT on infrastructure and integration, and product engineering on how model output lands in Navigator and Radar
Skills
- 3 to 6 years building and shipping machine learning models that real users depended on in production
- Strong Python. Fluent with the standard modeling stack: pandas, scikit-learn, and at least one of XGBoost, LightGBM, or PyTorch
- Hands-on experience building with LLMs beyond API calls: retrieval, structured output, prompt versioning, and evaluation of generative output
- SQL good enough to build your own features from a messy relational source without waiting on anyone
- Experience deploying models as services and monitoring them after launch, including at least one model you had to debug or retrain in production
- A working approach to evaluation. You build the test set before the model and you can explain your metric choices
- The habit of writing things down. Documentation, runbooks, and written analysis are part of how you work
- Remote, anywhere in the US
- Healthcare experience, especially revenue cycle, utilization management, CDI, coding, or payer operations
- Worked with clinical data standards: FHIR, HL7, ICD-10, CPT, MS-DRG, or claims data
- Built under a regulator: HIPAA, HITRUST, SOC 2, or FDA SaMD
- Salesforce data model exposure, or experience integrating ML output into a Salesforce-based application
- MLOps depth: CI for models, feature stores, experiment tracking, or containerized model serving
- Built explainability into a model that a non-technical domain expert had to trust and act on
- Experience with agent frameworks and multi-step LLM workflows running in production, past the prototype stage
Benefits
- Remote, anywhere in the US
- Health, dental, vision, and 401(k)
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