TL;DR

Staff Data Scientist (Data Science/ML): Lead design and development of scalable machine learning systems for menu recommendation, demand forecasting, offer targeting, and guest personalization with an accent on full ML lifecycle ownership, advanced modeling, and cross-team collaboration. Focus on designing production ML systems, setting best practices, mentoring, and leveraging cutting-edge AI tools to drive business impact and innovation.

Location: Hybrid in Toronto, Ontario, Canada

Salary: 120,000–192,000 CAD

Company

%hirify_global% creates technology to help restaurants and local businesses succeed in a digital world by empowering them with purpose-built technology to operate, increase sales, engage customers, and keep employees happy.

What you will do

Own the full machine learning lifecycle from problem framing to deployment and monitoring for mission-critical initiatives.
Design and implement advanced ML and statistical models to improve product performance and customer insights.
Collaborate with engineers, product managers, and stakeholders to define project scope and integration strategy.
Guide architectural decisions, set modeling standards, and champion best practices for experimentation and productionization.
Mentor data scientists and raise technical standards through reviews and knowledge sharing.
Identify business value opportunities for data science and lead cross-functional efforts to realize them.

Requirements

Must have 8+ years of experience in data science with production ML systems delivery.
Deep knowledge of statistical modeling, machine learning, and model evaluation.
Experience with distributed data processing, real-time inference, and ML Ops frameworks.
Proficiency in Python, SQL, and ML frameworks like scikit-learn, PyTorch, TensorFlow.
Hands-on experience with cloud platforms, preferably AWS and related tools.
Excellent communication skills to influence technical and non-technical stakeholders.

Nice to have

Advanced degree in Computer Science, Statistics, or related STEM field.
Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.
Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF).

Culture & Benefits

Hybrid work model fostering in-person collaboration and individual needs.
Competitive compensation and benefits supporting a healthy lifestyle and flexibility.
Culture of innovation embracing new AI tools and continuous learning.
Commitment to diversity, equity, and inclusion with equitable opportunities.