Website CYNET SYSTEMS
CYNET SYSTEMS
Company : CYNET SYSTEMS
Job Description:
Pay Range: $77.75hr – $82.75hr
- Lead the technical design, software development, and implementation of the data science, AI, ML, and generative AI capabilities for our consumer-centric digital health platform, aligning with business objectives and consumer needs.
- Lead the technical design and software development of federated data management and federated learning systems to support scalability of our digital health platform and to support patient ownership and control of healthcare data.
- Lead the vision and strategy for the technical design and development of AI, ML, and generative AI capabilities, incorporating emerging research and technologies and new methodologies in the AI/ML and generative AI space.
- Design and apply complex mathematical models and statistical methods to analyze datasets that include multi-modal clinical data (including patient-reported outcomes, remote patient monitoring device data, EHR data, and claims data), social determinants of health data, and consumer behavior data.
- Develop data engineering architectures and systems to ensure data quality, governance, and interoperability.
- Create predictive models to identify health and wellness risks, recommend interventions, and personalize care journeys.
- Develop innovative approaches to support patients, informal caregivers, and other individuals through data-driven health and wellness insights and tools.
- Design and implement AI-based agents and chatbots to enhance consumer engagement and to improve efficiency and productivity of care management and administrative team members.
- Perform feature engineering and selection to optimize model performance and extract meaningful insights from complex datasets, and develop automated feature engineering pipelines to ensure scalability and reproducibility.
- Rigorously evaluate and validate machine learning models using appropriate metrics and techniques, ensuring robustness, generalizability, and clinical relevance, and establish best practices for model validation, including the use of held-out datasets, cross-validation, and external validation.
- Collaborate with product, engineering, clinical, and behavioral science teams to develop actionable insights and intelligent features within patient- and caregiver-facing digital tools.
- Stay current with emerging research and technologies in data science, AI, and machine learning, and explore new methodologies and technologies to enhance our platform's capabilities.
Education and Experience:
- Advanced degree (MS or PhD) in Computer Science, Statistics, Applied Mathematics, Biostatistics, or related field.
- 5+ years of experience in developing data management systems for large volumes of data and applying complex mathematical and statistical methods to real-world problems.
- 2+ years of experience in developing data science, analytics, and AI/ML solutions.
- Proven track record developing and deploying machine learning models in production environments.
- Experience in Python, R, or similar languages, and experience with ML frameworks (TensorFlow, PyTorch).
Preferred:
- Experience with natural language processing, computer vision, or time-series analysis.
- Experience with healthcare data and analytics, or experience developing healthcare technology and applications.
- Understanding of healthcare data standards and healthcare workflows
Professional Skills / Competencies:
- Excellent communication skills with the ability to translate complex technical concepts to non-technical stakeholders.
- Work effectively in a matrixed organization, influencing stakeholders without
- direct authority.
- Strong negotiation, collaboration, and influence management skills.
- Excellent communication skills to articulate clinical concepts and rationale to
- stakeholders.
- Comfortable with ambiguity and adaptable to complex and dynamic situations.
- Multicultural awareness to enable collaboration with diverse, global teams.
- Passionate about improving access to care and healthcare experiences.