Website CYNET SYSTEMS
CYNET SYSTEMS
Company : CYNET SYSTEMS
Job Description:
Pay Range: $78.75hr – $82.75hr
Requirement/Must Have:
- PhD or Master s degree in Computer Science, Statistics, Biomedical Informatics, Engineering, Physics, or a related quantitative field.
- 8+ years of experience in data science, with strong expertise in predictive modeling and advanced analytics in healthcare or life sciences.
- Demonstrated hands-on experience with machine learning and deep learning algorithms, especially transformer architectures (e.g., BERT).
- Practical experience with tree-based models such as XGBoost.
- Proficiency in Python or R and use of major data science libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience working with real-world healthcare datasets such as open claims data, EHR, or clinical trial data.
Experience:
- Proven experience leading complex end-to-end data science projects from ideation to production.
- Deep understanding of statistical concepts, experimental design, and performance metrics such as Precision, Recall, Bayes Factor, and NNT.
- Familiarity with MLOps practices and deploying models into real-world environments.
- Exposure to federated analytics and secure federated learning platforms.
- Experience working with multidisciplinary teams across healthcare functions.
Responsibilities and Duties:
- Lead development of predictive models using machine learning and AI for healthcare use cases.
- Guide the strategic roadmap for data science initiatives in multiple therapy areas.
- Evaluate and recommend modeling techniques tailored to business and clinical goals.
- Mentor and lead a team of data scientists, promoting best practices and continuous learning.
- Collaborate with business owners, clinicians, payers, statisticians, and IT teams to build scalable solutions.
- Stay current on advancements in AI and proactively incorporate new technologies into practice.
- Communicate technical insights to non-technical stakeholders in a clear and actionable manner.
Should Have:
- Strategic mindset with a focus on patient outcomes and measurable business impact.
- Excellent leadership, interpersonal, and communication skills.
- Ability to influence and collaborate across cross-functional teams and organizational levels.
Skills:
- Machine Learning: XGBoost, scikit-learn, BERT, Transformer architectures.
- Deep Learning: PyTorch, TensorFlow.
- Programming: Python, R.
- Data: Claims data, EHR, real-world evidence.
- MLOps and deployment best practices.
- Federated Learning & Secure Data Analytics.
- Statistical Analysis and Experimental Design.
Qualification and Education:
-
Master s or PhD in Computer Science, Statistics, Biomedical Informatics, or related field.