Staff Data Scientist, Applied ML

Are you a Machine Learning practitioner who is technically deep and commercially sharp? If so, this might be the role for you! 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. That's why we put the power and flexibility in their hands to run their businesses how, where, and when they want!

Our culture of transparency, inclusivity, collaboration, and innovation has been recognized by Great Place to Work, Canada's Most Admired Corporate Cultures, and more. Jobber has also been named on the Globe and Mail's Canada's Top Growing Companies list, and Deloitte Canada's Technology Fast 50™, Enterprise Fast 15, and Technology Fast 500™ lists. 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; we have a ton of onboarding resources, tutorials, hackathons and buddies to support learnings and provide opportunities to innovate. We have a range of experience levels on teams which allows for mentor/mentee opportunities. Leaders at Jobber work with empathy and support employees to build healthy work-life harmony. Bring your dedication and passion to this job to fulfill your goals.

The team:

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. We're a small, hungry team with an outsized footprint. We work in two-week sprints, demo to the whole department, and expect each other to bring a point of view rather than a ticket.

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.

The role: 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..

Design, build, and evaluate retrieval-augmented generation (RAG) systems on top of that graph. 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. When a model is live, its uptime, latency, and accuracy are yours.

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.

Stay current, and bring it back. 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.

To be successful, you should have:

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. You can reason about bias and variance, justify a loss function, and tell signal from noise without hand-waving.

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, with hands-on experience applying LLMs in production systems, including RAG and context management.

Experience designing for the real cost of being wrong. You've built or tuned custom and asymmetric loss functions where a miss in one direction is far more expensive than the other, and you can explain the business reasoning behind the choice.