CalTek is an engineering and IT staffing agency that specializes in recruiting healthcare and pharmaceutical personnel for its clients. Our client is a pioneering medical technology company focused on improving the lives of individuals with diabetes through cutting-edge Continuous Glucose Monitoring (CGM) systems. With a mission rooted in patient outcomes and preventive care, the company designs, develops, and manufactures state-of-the-art wearable medical devices that deliver real-time glucose monitoring and data-driven insights. Their FDA-cleared and globally distributed products are at the intersection of biomedical engineering, AI-driven health analytics, and wearable IoT technology.
To support their growing data ecosystem and accelerate innovation in personalized diabetes care, the company has asked us to recruit an experienced Data Scientist to develop predictive models, analyze real-world usage data, and enable impactful health outcomes through machine learning and advanced analytics.
Typical Duties and Responsibilities
- Design, develop, and validate machine learning models and statistical algorithms for glucose trend prediction, alert systems, and user behavior analysis.
- Analyze large datasets generated from CGM devices, mobile apps, EHR integrations, and cloud-based platforms to identify actionable insights that improve clinical outcomes.
- Collaborate with cross-functional teams including Biomedical Engineers, Software Developers, Clinical Researchers, and Regulatory Affairs to translate data insights into product enhancements and patient features.
- Build end-to-end data pipelines for ingesting, cleaning, transforming, and analyzing real-time and historical health data.
- Develop data visualization tools and dashboards using Python, Tableau, or Power BI to communicate findings to stakeholders.
- Ensure model interpretability, clinical relevance, and alignment with medical device regulations such as FDA 21 CFR Part 820, ISO 13485, and GDPR/HIPAA.
- Assist in the design of clinical studies and post-market surveillance strategies by analyzing user trends, device performance, and adherence data.
- Maintain rigorous data documentation and participate in peer reviews to support reproducibility and compliance.
Education
- Master’s Degree in Data Science, Computer Science, Statistics, Biomedical Engineering, or a related quantitative field is required.
- Ph.D. preferred, especially with a focus in medical data analytics, biostatistics, or applied machine learning in healthcare settings.
Required Skills and Experience
- 5+ years of experience in data science, machine learning, or health analytics, ideally within a regulated medical device, biotech, or healthtech environment.
- Strong proficiency in programming languages and data science tools including:
- Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch)
- R, SQL, and Jupyter Notebooks
- Version control with Git/GitHub
- Experience with time-series data analysis, signal processing, and real-time model deployment.
- Familiarity with cloud platforms such as AWS, Azure, or GCP for large-scale data processing and ML model hosting.
- Understanding of statistical inference, experimental design, and clinical trial data interpretation.
- Prior exposure to CGM, wearable devices, or physiological sensor data is a significant advantage.
- Knowledge of privacy regulations (HIPAA, GDPR) and standards for medical data integrity and traceability.
Preferred Qualifications
- Experience working with data from IoT medical devices, Bluetooth-enabled sensors, or mobile health apps.
- Familiarity with tools such as Snowflake, Databricks, Apache Spark, or Kubernetes for scalable ML deployment.
- Background in Digital Health, Remote Patient Monitoring (RPM), or population health analytics.
- Published work or patents in the field of predictive modeling or applied healthcare AI is a strong plus.
- Certifications in AI for Healthcare, TensorFlow Developer, or Microsoft Certified: Azure AI Engineer Associate are valued.
