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Salary: CAD 145,900 - 197,400 / annual
Jobber is a SaaS platform serving small home service businesses (plumbers, painters, landscapers) with tools for quoting, scheduling, invoicing, and payments. The company operates across hundreds of thousands of service professionals and millions of their clients, generating high-velocity SaaS event data, scheduling data, and payments data.
The Staff Data Scientist, Applied Machine Learning is a senior individual contributor role reporting to the Manager, Data Science. This is an architectural-scope position responsible for designing, building, and maintaining ML systems for automated decisioning at scale.
Key responsibilities include:
- Continuing to build and improve SignalGraph, a company-wide metric graph connecting organizational activity and outcomes. You will evolve metric-family contracts, maintain segment and causal-edge catalogs, operate the Neo4j and series-refresh pipeline, and strengthen investigation diagnostics so teams can trace metric changes to root causes.
- Designing, building, and evaluating retrieval-augmented generation (RAG) systems on top of the metric graph. You own retrieval quality, context management, and the evaluation harness that proves the system works correctly, not just fluently.
- Owning production ML end-to-end: training pipelines, real-time serving, monitoring, drift detection, and retraining. When a model is live, its uptime, latency, and accuracy are your responsibility.
- Establishing systematic evaluation and regression testing as a standard for the team so model and LLM system quality is measured and defended over time rather than assessed once at launch.
- Setting the technical bar and multiplying the team: driving MLOps and ML engineering standards, shaping the feature store and platform roadmap, reviewing peers' work, and mentoring other scientists on graph and deep learning methods.
- Partnering directly with senior leadership. Your work will be used by Senior Leaders, Customer Analytics, Business Intelligence, and Product to make decisions against Jobber's North Star goals. You'll present your own work, defend your assumptions, and help cross-functional partners reframe their approach.
- Staying current on AI/ML developments methodologically and translating that into shipped capability.
The role works in two-week sprints, demos to the whole department, and expects team members to bring a point of view rather than just execute tickets.
REQUIREMENTS:
Must-have experience:
- Production ML experience, end-to-end: training, deploying, serving, and maintaining models that acted on real users (not just offline scoring). You understand retraining, drift, and technical debt from real-world experience.
- Strong statistics foundation: ability to reason about bias and variance, justify loss functions, and distinguish signal from noise.
- Expert SQL and production-grade Python skills.
- Depth in modern ML methods: deep learning and neural architectures, transformers/BERT-family models, RNN/CNN, ranking, and representation learning. Hands-on experience applying LLMs in production systems, including RAG and context management.
- Experience designing for the real cost of being wrong: building or tuning custom and asymmetric loss functions where misses in one direction are far more expensive than the other.
- Experience with large data in production environments and supporting platforms: ML/AI platforms such as Snowflake, orchestration with Apache Airflow, and cloud infrastructure (AWS strongly preferred; GCP or Azure equivalent considered).
- Strong communication and stakeholder alignment skills: ability to take technical and non-technical partners from confusion to alignment, present uncertainty honestly, and influence without authority.
- Ownership of quality over shipping speed: you want to understand why something works and how to make it better. You don't accept current model performance as a ceiling and don't outsource your understanding to AI assistants.
Nice-to-have experience:
- Graph experience: graph theory, graph neural networks, knowledge graphs, or graph databases such as Neo4j.
- Software engineering background: services and model serving (REST/gRPC), Docker/Kubernetes, CI/CD, feature stores.
- Experience with Snowflake, including Snowpark and Snowpark Container Services, or comparable warehouse-to-deployed-model workflows.
- Experience building LLM evaluation infrastructure, safety layers, or LLMOps for customer-facing AI.
- Exposure to risk, fraud, or fintech modeling, or to recommendation and ranking systems at scale.
