- Company Name
- CINQCARE
- Job Title
- Senior Healthcare Data Scientist - AI/ML
- Job Description
-
Job title: Senior Healthcare Data Scientist – AI/ML
Role Summary: Lead the design, development, and deployment of advanced machine learning models and analytical solutions to inform personalized care programs and outreach initiatives. Drive lifecycle management of models, ensure equitable outcomes, and translate complex analytics into actionable insights for clinicians, engagement teams, and leadership.
Expectations: Deliver production-ready predictive models that stratify risk, improve patient targeting, and predict engagement. Maintain model health through monitoring, retraining, and drift detection. Apply fairness, bias mitigation, and explainability frameworks to guarantee equity and trustworthiness. Collaborate with cross-functional teams and iterate on emerging AI methods (e.g., generative AI, causal inference, federated learning) to enhance community-based care.
Key Responsibilities:
- Integrate and curate analytical datasets from EHRs, claims, engagement platforms, SDOH sources, and commercial data vendors.
- Build, evaluate, and manage predictive models (classification, regression, NLP, time-series, survival analysis, agentic modeling).
- Own full model lifecycle: development, deployment, monitoring, retraining, drift detection.
- Apply fairness, bias mitigation, and explainability to all models.
- Conduct scenario modeling and simulations to support clinical and business decisions.
- Incorporate engagement data into continuous feedback loops for model refinement.
- Translate analyses into clear narratives and visualizations for stakeholders.
- Develop dashboards, reports, and self‑service analytics tools.
- Partner with engineering for scalable MLOps deployment.
- Collaborate with clinicians, engagement specialists, and leadership to align data science outcomes with organizational priorities.
- Research and pilot emerging methods relevant to health equity and community-based care.
Required Skills:
- Advanced proficiency in Python, SQL, and ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch).
- Expertise in NLP, time-series modeling, survival analysis, and agentic modeling.
- Strong MLOps practices, including containerization, CI/CD, model monitoring, and drift detection.
- Experience with cloud platforms (Azure preferred; AWS/GCP acceptable).
- Advanced visualization skills with Tableau, Power BI, Plotly, or Dash.
- Excellent communication and data storytelling for technical and non‑technical audiences.
- Demonstrated ability to apply fairness, bias mitigation, and explainability frameworks.
Required Education & Certifications:
- Master’s or PhD in Data Science, Statistics, Computer Science, Economics, or related quantitative field (Bachelor’s with significant applied experience considered).
- Minimum 7 years of applied data science experience, preferably in healthcare, public health, insurance, or digital health.