Job Description:
Key Responsibilities:
- Develop Predictive Models: Build and deploy AI/ML models to detect patterns related to loneliness, cognitive decline, and behavioral changes.
- Design Personalization Algorithms: Create recommendation systems to match users with relevant social activities, cognitive exercises, or community groups.
- Behavioral Insights Analysis: Develop models to analyze user engagement patterns and identify triggers for disengagement.
- Integration with App Features: Work with engineering teams to integrate AI-driven insights seamlessly into the app (e.g., mood tracking, cognitive assessments).
- Data Pipeline Management: Create and maintain data pipelines to collect, preprocess, and train on structured and unstructured data (e.g., surveys, app activity logs).
- Collaborate with Research Partners: Work alongside researchers and universities to validate models and incorporate cognitive science findings into ML solutions.
- Monitor and Optimize Models: Continuously evaluate and optimize models for performance, accuracy, and fairness, ensuring compliance with ethical AI guidelines.
- User Privacy and Data Security: Ensure adherence to data privacy laws (HIPAA, GDPR) while processing user data.
Qualifications:
- Education:
- Bachelor's or master's degree in computer science, Data Science, Machine Learning, or related fields.
Technical Skills:
- Proficient in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Experience with NLP, recommendation systems, and predictive modeling.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps tools for deployment and monitoring.
- Solid understanding of data privacy regulations and ethical AI practices.
Experience:
- 3+ years of experience in developing and deploying AI/ML models.
- Experience in healthtech or wellness-focused applications is a plus.
- Proven track record in personalization algorithms and behavioral analytics is desirable
Soft Skills:
- Strong collaboration skills with cross-functional teams (product, healthcare, and research partners).
- Passion for improving mental well-being and social engagement through technology.
- Ability to communicate complex technical concepts to non-technical stakeholders.
Skill:
Machine Learning, Cloud, Docker, Kubernetes, Natural Language Processing
