Data Scientist

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

1 Payer Propensity Modeling - predicting payer behavior and reimbursement likelihood

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

3 Payment Amount Prediction - forecasting expected reimbursement amounts

4 Cash Flow Forecasting - predicting revenue timing and patterns

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

6 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

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

2 Development Tools : Jupyter Notebooks, Git, Docker

3 Programming : Python, SQL, R (optional)

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

5 Data Processing : Spark, Pandas, NumPy

6 Visualization : Matplotlib, Seaborn, Plotly, Tableau