Jobber is hiring a Staff Data Scientist Applied ML in Toronto!
Jobber is hiring a Staff Data Scientist, Applied Machine Learning in Toronto to own models and systems for automated decisioning. This is a senior individual-contributor role with architectural scope: the successful candidate will shape how ML systems are built, evaluated, served, monitored, and trusted across Jobber's product and analytics environment. The work centers on SignalGraph, a company-wide metric graph, plus retrieval-augmented generation on top of that graph, production model ownership, and systematic evaluation standards. The role draws on high-volume SaaS event, scheduling, payments, and unstructured sales/support data at a scale uncommon for a company of Jobber's size. The scientist will also mentor peers, set MLOps and ML engineering standards, review others' work, and present directly to senior leaders and cross-functional partners. Jobber describes the team as small but high-impact, working in two-week sprints and expecting independent points of view rather than ticket-taking.
Key responsibilities
- Evolve and maintain the SignalGraph metric graph, including metric-family contracts, segment and causal-edge catalogs, Neo4j and refresh pipelines, and investigation diagnostics.
- Design, build, and evaluate retrieval-augmented generation systems on top of the graph, owning retrieval quality, context management, and evaluation harnesses.
- Own production ML end-to-end: training pipelines, real-time serving, monitoring, drift detection, and retraining, with accountability for uptime, latency, and accuracy.
- Establish systematic evaluation and regression testing for models and LLM systems so quality is measured and defended over time.
- Set technical standards for MLOps and ML engineering, influence feature-store and platform roadmaps, review peers' work, and mentor scientists on graph and deep-learning methods.
- Partner with senior leadership, Customer Analytics, Business Intelligence, and Product teams, presenting work and defending assumptions to influence decisions.
- Track AI/ML developments and translate them into shipped capabilities.
Key requirements
- End-to-end production ML experience, including training, deployment, serving, and maintenance of models that act on real users.
- Strong statistics foundation, including bias/variance reasoning, loss-function justification, and signal-versus-noise judgment.
- Expert SQL and production-grade Python skills.
- Depth in modern ML methods such as deep learning, transformers/BERT-family models, RNN/CNN, ranking, and representation learning, plus hands-on LLM production experience including RAG.
- Experience with asymmetric or custom loss functions and the business reasoning behind them.
- Experience with large production data and platforms such as Snowflake, Apache Airflow, and AWS, with GCP or Azure considered equivalent.
- Strong communication and stakeholder alignment skills, including presenting uncertainty honestly and influencing without authority.
- Commitment to quality and deep understanding over shipping speed alone.
- Seniority: Staff / senior individual contributor
- Work mode: Not specified
- Salary: CAD $145,900–$197,400 annually (minimum $145,900; midpoint $171,600; maximum $197,400)
- Key skills: Python, SQL, Deep learning, Transformers, LLMs, RAG, MLOps, Graph representation learning, Ranking, Neo4j, Snowflake, Apache Airflow, AWS, Model serving, Monitoring and drift detection
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