Smilepass is a pioneering startup looking to shake things up in the dental industry, and other healthcare verticals. We are a team of diverse backgrounds and expertise who are driven by a desire to collaborate and innovate. As a company, we believe in building long-lasting client partnerships and focusing on alignment in goals and objectives. Our mission is to promote patients’ access to personalized and economical payment options and empower healthcare providers through efficiency and automation.
As a valued member of our team, you will play a critical role in architecting and developing Smilepass’s application of massive datasets to derive actionable insights, advancing our mission to make healthcare more affordable and accessible.
Responsibilities
- Design and conduct Machine Learning experiments to address crucial challenges in healthcare affordability and accessibility.
- Collaborate with the data pipeline and ML engineering team to understand datasets, propose appropriate machine learning models, and implement them effectively.
- Partner with product and engineering teams to explore AI/ML solutions for various healthcare problems, determining when simpler statistical methods might be more suitable.
- Define and evaluate data and model quality, identifying the most effective models and techniques for our domain.
- Work closely with the development and ML engineering team to develop, implement, train, test, and deploy ML solutions.
Qualifications
- Affiliation with an academic institution (preferably the University of Toronto)
- At least 4 years of applied artificial intelligence experience with a focus on machine learning and ML Ops (research experience is qualifying).
- Proficiency in working with large datasets and utilizing tools for data ingestion, transformation, analysis, and processing at scale.
- Expertise in Python, SciPy, Numpy, Pandas, Relational DBs, Git/Source-control, Data Processing, Deep Learning, Supervised and Unsupervised ML algorithms, Tensorflow/PyTorch/Keras, statistics, and optimization in mathematics.
- Experience with live A/B testing at scale.
- Ability to work in an agile team using Scrum and/or Kanban methodologies.
- Comfortable participating in daily stand-ups, sprint planning sessions, and retrospectives.
- Demonstrated commitment to continuous learning and skill enhancement.
