Key Responsibilities

As the Lead Data Scientist, your role will require you to work closely with subject matter experts in clinical and financial administration across practices, health systems, hospitals, and payors. Your machine learning projects will span the entire healthcare revenue cycle - from clinical encounters through financial transaction completion, extending into back-office operations and payer interactions.

You will lead the development of predictive machine learning models for Revenue Cycle Management analytics, along the lines of:

·     Payer Propensity Modeling - predicting payer behavior and reimbursement likelihood

·     Claim Denials Prediction - identifying high-risk claims before submission

·     Payment Amount Prediction - forecasting expected reimbursement amounts

·     Cash Flow Forecasting - predicting revenue timing and patterns

·     Patient-Related Models - enhancing patient financial experience and outcomes

·     Claim Processing Time Prediction - optimizing workflow and resource allocation

Additionally, we will work on emerging areas and integration opportunities—for example, denial prediction + appeal success probability or prior authorization prediction + approval likelihood models.

You will reimagine how providers, patients, and payors interact within the healthcare ecosystem through intelligent automation and predictive insights, ensuring that providers can focus on delivering the highest quality patient care.

VHT Technical Environment

·     Cloud Platform: AWS (SageMaker, S3, Redshift, EC2)

·     Development Tools: Jupyter Notebooks, Git, Docker

·     Programming: Python, SQL, R (optional)

·     ML/AI Stack: Scikit-learn, TensorFlow/PyTorch, MLflow, Airflow

·     Data Processing: Spark, Pandas, NumPy

·     Visualization: Matplotlib, Seaborn, Plotly, Tableau

Required Qualifications

You should be someone who thrives on solving complex problems and has a natural attraction to ill-defined challenges that seem intractable, transforming them into elegant data science solutions. You find yourself comfortable in the middle of what seems to be chaos, knowing you can discover patterns and create order through data. You want to create a meaningful impact by solving complex healthcare problems through your passion for data science and machine learning.

·     Advanced degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field

·     5+ years of hands-on data science experience with a proven track record of deploying ML models to production

·     Expert-level proficiency in SQL and Python, with extensive experience using standard Python machine learning libraries (scikit-learn, pandas, numpy, matplotlib, seaborn, etc.)

·     Cloud platform experience, preferably AWS, with hands-on knowledge of SageMaker, S3, Redshift, and Jupyter Notebook workbenches (other cloud environments acceptable)

·     Strong statistical modeling and machine learning expertise across supervised and unsupervised learning techniques

·     Experience with model deployment, monitoring, and MLOps practices

·     Excellent communication skills with the ability to translate complex technical concepts to non-technical stakeholders

Preferred Qualifications

·     US Healthcare industry experience, particularly in Health Insurance and/or Medical Revenue Cycle Management

·     Experience with healthcare data standards (HL7, FHIR, X12 EDI)

·     Knowledge of healthcare regulations (HIPAA, compliance requirements)

·     Experience with deep learning frameworks (TensorFlow, PyTorch)

·     Familiarity with real-time streaming data processing

·     Previous leadership or mentoring experience

Compensation and Benefits:

The position pays a fixed salary, plus a potential annual bonus driven by the success of the individual, team, and company. Other benefits include: