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Predictive Analytics Engineer

About Us

ÆONIS SYSTEMS is looking for a Predictive Analytics Engineer to develop AI-driven risk prediction models for healthcare and education. This role focuses on forecasting patient deterioration, optimizing hospital workflows, and improving personalized learning experiences. The ideal candidate has expertise in statistical modeling, machine learning, and real-time data analysis.

Important Note: Until funding is secured, we ask for a commitment of 10-15 hours per month to ensure steady progress while respecting your time and contributions. This role is open to global applicants and can be remote.

Key Responsibilities

  • Risk Prediction Models: Develop AI-driven predictive analytics for patient monitoring, disease progression, and early risk detection.

  • Healthcare & Education Data Analysis: Design machine learning models to forecast patient readmission risks, treatment responses, and academic performance trends.

  • Time-Series & Statistical Modeling: Implement regression models, anomaly detection, and probabilistic forecasting techniques.

  • Big Data Handling: Work with large-scale healthcare and educational datasets to build reliable predictive solutions.

  • Model Deployment & Optimization: Ensure models run efficiently in real-time applications, integrating with clinical decision support systems and adaptive learning platforms.

  • Collaboration: Work closely with clinicians, hospital administrators, and educators to refine predictive models.

Qualifications

  • Experience: Minimum 3+ years in predictive analytics, time-series forecasting, or real-time ML applications.

  • Academic & Technical Background: Bachelor’s, Master’s, or PhD in Computer Science, AI, Computational Linguistics, or related fields.

  • Cloud & MLOps Knowledge: Familiarity with AWS, GCP, Azure, or scalable cloud-based predictive analytics solutions.

  • Programming Skills: Proficiency in Python, R, SQL, TensorFlow, PyTorch, or similar frameworks.

  • Data Engineering: Experience in handling structured & unstructured datasets, feature engineering, and data wrangling.

  • Healthcare & Education AI: Bonus points for experience with electronic health records (EHR), student performance datasets, or real-time analytics.

Work Model & Expectations

  • Commitment: 10-15 hours per month.

  • Work Type: Remote

  • Location: Open to global applicants.

  • Compensation: Currently voluntary; early contributors will be prioritized for paid roles once funding is secured

Let’s work together.

If you're passionate about data-driven forecasting, AI-powered risk prediction, and real-time decision-making, apply today and help shape the future of predictive analytics!