Data Scientist

Key Responsibilities

• Data Engineering & Wrangling: Collect, clean, and preprocess large healthcare datasets, including claims, eligibility, provider, and reimbursement data.
• Model Development: Design and implement machine learning models to address fraud detection, payment integrity, risk scoring, and utilization trends.
• Healthcare Analytics: Develop advanced analytics to improve cost efficiency, provider performance, and payer reimbursement strategies.
• Python Development: Build scalable data pipelines and automation scripts for model training, deployment, and monitoring.
• Statistical & Predictive Modeling: Apply advanced statistical techniques, predictive modeling, and AI/ML methodologies to generate actionable insights.
• Data Visualization & Reporting: Develop dashboards and reports using Python, Power BI, or Tableau to present findings to stakeholders.
• Collaboration & Communication: Work cross-functionally with product teams, actuarial analysts, engineers, and business leaders to translate business needs into data-driven solutions.
• Regulatory & Compliance Awareness: Ensure models align with CMS, HIPAA, and healthcare regulatory frameworks.

Required Qualifications

• Experience: 5+ years in data science, machine learning, and Python-based data engineering within the healthcare payor space.
• Technical Skills:
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Flask/FastAPI)
• Experience with SQL and NoSQL databases (PostgreSQL, Snowflake, BigQuery, MongoDB, etc.)
• Hands-on experience with ETL processes, data pipelines, and cloud platforms (AWS, GCP, or Azure)
• Knowledge of Big Data technologies (Spark, Databricks, or Hadoop) is a plus
• Domain Expertise: Deep understanding of claims processing, risk adjustment, provider reimbursement models, and fraud detection.
• Machine Learning & AI: Experience developing supervised and unsupervised models, NLP applications, and anomaly detection algorithms.
• Data Governance & Security: Understanding of HIPAA, CMS regulations, and healthcare compliance requirements.
• Soft Skills: Strong analytical mindset, problem-solving abilities, and the ability to communicate complex insights to non-technical stakeholders.

Preferred Qualifications

• Experience with FHIR, HL7, or other healthcare interoperability standards.
• Familiarity with payer-provider data exchange models and value-based care analytics.
• Experience working with CMS claims data, MCO data, or Medicare Advantage risk models