About Rhapsody:
We all know that our health care system is complicated. Getting data from one provider to another, or from a provider to a health insurance company, is frustrating for virtually everyone. Imagine developing solutions that help make these data transactions easier and faster. That is what we do at Rhapsody; we make interoperability platforms that allow data – such as patient encounter details, lab results, and billing information – to move seamlessly from one system to another.
While most people will not ever see our products and services during a medical encounter, our interoperability platforms are running behind the scenes. Think of them as the central nervous system helping to move data where it needs to be to improve the patient experience. To learn more about Rhapsody, visit www.rhapsody.health
If using your expertise in a way that impacts our health care system, patient care, and population health sounds like something you would find rewarding, apply today!
This is a remote position. However, preference is for someone local to Dallas, TX.
Position Summary:
Rhapsody is expanding its AI and data science capabilities to drive experimentation, predictive modeling, system evaluation, and actionable insights across our product ecosystem. We’re hiring a Data Scientist to contribute to high-impact initiatives—including ML-based patient matching, churn prediction, forecasting, and GenAI workflow support.
You’ll work closely with AI and engineering teammates to evaluate models, build predictive solutions, and uncover insights that improve both product performance and internal decision-making. This role offers the opportunity to work across a wide range of data science projects—from improving patient-matching logic to analyzing GenAI behavior—and to help shape how data science evolves across the company.
Key Responsibilities:
- Analyze outputs and feedback data from AI systems to identify quality gaps, failure patterns, and opportunities for improvement; help define signals and metrics that guide model refinement and evaluation
- Contribute to the development and evaluation of the patient matching system, including analysis of existing logic, feature design, model exploration, and performance improvement
- Build and evaluate predictive models for use cases such as churn, revenue forecasting, and risk scoring
- Collaborate on model evaluation pipelines for both structured ML and GenAI workflows
- Uncover patterns in data and identify opportunities where data science techniques can improve existing workflows or decision logic—even when the path isn’t strictly model-driven
- Analyze system behavior through performance metrics, interaction logs, and failure patterns to guide iteration
- Develop dashboards, reports, and datasets to inform AI, product, and business teams
- Collaborate with AI Engineers on LLM and GenAI-related initiatives
- Learn and apply best practices in testing, validation, and explainability for ML and AI-driven systems
Qualifications:
Required:
- 3+ years of experience in data science, machine learning, or applied analytics
- Strong Python skills (e.g., pandas, NumPy, matplotlib, scikit-learn)
- Strong data analysis skills, with experience exploring, interpreting, and extracting insights from complex datasets
- Familiarity with SQL
- Experience working with structured data and building basic models (e.g., classification, regression)
- Curiosity about AI/ML systems and a willingness to learn quickly through real-world application
Preferred:
- Exposure to evaluating or experimenting with neural networks
- Familiarity with model evaluation techniques (e.g., precision/recall, ROC, drift detection)
- Interest in or exposure to GenAI workflows (e.g., prompt evaluation, embedding inspection)
- Experience using tools like Jupyter, MLflow, Airflow, or analytics platforms
Education:
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field (or equivalent experience)
