Sr. Data Scientist

This is a key position that will play a pivotal role in creating data-driven technology solutions to establish our client as a leader in healthcare, financial, and clinical administration. As the Lead Data Scientist, you will be instrumental in building and implementing machine learning models and predictive analytics solutions that will spearhead the new era of AI-driven innovation in the healthcare industry. Your responsibilities will involve developing and implementing a variety of ML/AI products, from conceptualization to production, to help the organization gain a competitive edge in the market. Working closely with the Director of Data Science, you will operate at the crossroads of healthcare, finance, and cutting-edge data science to tackle some of the most intricate challenges faced by the industry. This role presents a unique opportunity within VHT's Product Transformation division to create pioneering machine learning capabilities from scratch. You will have the chance to shape the future of VHT's data science & analytics foundation, utilizing state-of-the-art tools and methodologies within a collaborative and innovation-focused environment. Key Responsibilities: - Lead the development of predictive machine learning models for Revenue Cycle Management analytics, focusing on areas such as: - Claim Denials Prediction: identifying high-risk claims before submission - 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 - Explore emerging areas and integration opportunities, e.g., denial prediction + appeal success probability or prior authorization prediction + approval likelihood models. 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: - 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,