Are you a Machine Learning practitioner who is technically deep and commercially sharp? We're looking for a Staff Data Scientist, Applied Machine Learning (ML) to join our growing Data Science team. Jobber exists to help people in small businesses be successful.

We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber they can quote, schedule, invoice, and collect payments from their customers, while providing an easy and professional customer experience. Running a small business today isn't like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more.

Our culture of transparency, inclusivity, collaboration, and innovation has been recognized by Great Place to Work, Canada's Most Admired Corporate Cultures, and more. With an Executive team that has over thirty years of industry experience of leading the way, we've come a long way from our first customer in 2011—but we've just scratched the surface of what we want to accomplish for our customers. We help employees grow professionally; Similar to how Jobber empowers small businesses with the tools and insights they need to succeed, the Strategy and Analytics Department ensures our people at Jobber have the tooling, data insights, and strategic direction to excel in our shared mission.

We turn data into actionable insights, and critical business needs into impactful software, working with multiple teams and departments across the company. Strategy & Analytics serves as a central hub that drives business outcomes in all corners of Jobber's ecosystem. Within that department, Data Science is the predictive and prescriptive arm.

SignalGraph is our next major project: a company-wide metric graph that connects organizational activity and outcomes so teams can trace metric movement to root causes, investigate faster, and make better decisions. Reporting to the Manager, Data Science, the Staff Data Scientist, Applied Machine Learning will own models and systems for automated decisioning. You will train and evaluate models using techniques such as graph representation learning, transformer architectures, and ranking, and engineer them to serve reliably in real time and at scale inside Jobber's product.

This is a senior individual contributor role with strong architectural scope. You'll be the person who decides how ML systems at Jobber are built, evaluated, and trusted and you'll be doing it against a dataset most companies our size don't have: high-velocity SaaS event data, scheduling data,



and payments data across hundreds of thousands of service professionals and millions of their clients, plus a rich body of unstructured sales and support text.

The Staff Data

Scientist, Applied Machine Learning will: Continue building, improving, and maintaining SignalGraph: evolve metric-family contracts, keep segment and causal-edge catalogs trustworthy, operate the Neo4j and series-refresh pipeline, and strengthen investigation diagnostics so teams can trace metric changes to root causes and make faster decisions from governed evidence. You’ll own retrieval quality, context management, and the evaluation harness that proves the system is actually right, not just fluent. Own production ML end-to-end : training pipelines, real-time serving, monitoring, drift detection, and retraining.

Establish 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.

Set the technical bar and multiply the team : drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph and deep learning methods. Partner directly with senior leadership. Much of what you build 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 how they approach a problem. We expect this role to be the most informed person at Jobber on developments in AI/ML methodologically, not just as a consumer of tools, and to translate that into shipped capability. Production ML experience, end-to-end.

You've trained, deployed, served, and maintained models that acted on real users (not just scored offline) You've learned the hard lessons about retraining, drift, and technical debt that come with it. A strong statistics foundation. Expert SQL and production-grade Python skills.

Depth in modern ML methods : Experience with large data in production environments and the platforms that support it . ML/AI platforms such as Snowflake, orchestration with Apache Airflow,



and cloud infrastructure (AWS strongly preferred; This team's work only lands if everyone shares the same understanding of what the data means. You can take a room of technical and non-technical partners from confusion to alignment, present uncertainty honestly, and influence without authority.

Ownership of quality over shipping speed alone. You don't accept a model's current performance as its ceiling, and you don't outsource your understanding of your own code to an AI assistant.

It would be nice if you had: Graph theory, graph neural networks, knowledge graphs, or graph databases such as Neo4j.

A software engineering background : Services and model serving (REST/gRPC), Docker/Kubernetes, CI/CD, feature stores.

Experience building LLM evaluation infrastructure, safety layers, or LLMOps for customer-facing AI. Exposure to risk, fraud, or fintech modelling , or to recommendation and ranking systems at scale. This role has a minimum annual salary of $145,900 CAD, a midpoint of $171,600 CAD and a maximum salary of $197,400 CAD designed to reflect the progression from learning the ropes to truly excelling.

We design our compensation to reflect each new hire's skills, experience, and the complexity of the role, ensuring a fair and market-competitive salary. Our range is intentionally broad to support growth and long-term impact, with fully established hires typically starting around the midpoint. Base salary is just one part of a total compensation package that will include equity rewards, annual stipends for health and wellness, retirement savings matching, and an extended health package with fully paid premiums for body and mind.

A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential. To work with a group of people who are humble, supportive, and give a sh*t about our customers. We are an equal opportunity employer, and we are committed to working with applicants requesting accommodation at any stage of the hiring process.

Job by job, we’re transforming the way service is delivered. Your lawn care provider, home cleaning service, plumber or painter could use Jobber to better connect with their customers, save time in the office, invoice faster, and get paid! We’re bringing tens of thousands of people together with technology to deliver billions of dollars a year in services to happy customers.

Jobber exists to help make these small businesses successful, and when they’re successful we all win! #

📌 Staff Data Scientist, Applied ML (Ontario)
🏢 Jobber
📍 Ontario