Staff Data Scientist, Applied Machine Learning

About the Role

We are searching for a highly skilled and commercially astute Staff Data Scientist specializing in Applied Machine Learning to join our expanding Data Science team. In this position, you'll work at the intersection of sophisticated ML techniques and real-world business challenges, helping empower small service businesses to thrive through technology.

About Jobber

Jobber provides software solutions designed to help small home service businesses—such as plumbers, painters, and landscapers—manage quoting, scheduling, invoicing, and payments, delivering an efficient and professional customer experience. As service delivery evolves rapidly, Jobber equips businesses with tools to operate flexibly and successfully.

Our culture promotes transparency, inclusivity, collaboration, and innovation, earning recognition from esteemed organizations and making Jobber stand out in Canada's tech ecosystem. We foster employee growth through onboarding resources, tutorials, hackathons, mentorship, and supportive leadership focused on a healthy work-life balance.

Department Overview

The Strategy and Analytics Department supports Jobber’s mission by delivering data tools, insights, and strategic guidance company-wide. The Data Science team, a focused group within, drives predictive and prescriptive analytics. We work iteratively in two-week sprints, encourage ownership of outcomes, and tackle impactful problems.

A flagship initiative, SignalGraph, is a comprehensive metric graph connecting operational activity to business outcomes, enabling faster root-cause analysis and informed decision-making.

Key Responsibilities

• Maintain and advance SignalGraph by evolving metric contracts, updating segment and causal-edge catalogs, operating Neo4j and pipeline refresh workflows, and enhancing diagnostic tools to facilitate evidence-based decision making.
• Design, develop, and evaluate retrieval-augmented generation (RAG) systems atop the graph, ensuring retrieval accuracy, context handling, and robust evaluation mechanisms that verify system correctness.
• Manage end-to-end production ML workflows, including training, real-time deployment, monitoring, drift detection, and retraining to guarantee model uptime, low latency, and accuracy.
• Instill systematic evaluation and regression testing as a standard practice to ensure continuous model quality over time.
• Lead technical standards in MLOps and ML engineering, shape feature store and platform roadmaps, conduct peer reviews, and mentor team members on graph and deep learning techniques.
• Engage directly with senior leadership and cross-functional stakeholders, presenting findings, defending methodologies, and guiding problem reframing aligned with company goals.
• Stay abreast of advances in AI/ML methodologies, not just as a tool user but as an innovator, integrating novel capabilities into delivered solutions.

Candidate Qualifications

• Proven experience delivering production ML pipelines from training through live serving and maintenance, with familiarity handling retraining challenges and model drift.
• Strong statistical knowledge enabling thoughtful tradeoffs between bias and variance, justified loss function selection, and reliable signal discernment.
• Expertise in SQL and production-quality Python development.
• Advanced skills in modern machine learning frameworks encompassing deep learning architectures, transformers (BERT-family), RNNs, CNNs, ranking models, representation learning, and applied large language model (LLM) integration including RAG systems.
• Experience designing asymmetric/custom loss functions aligned with real-world business costs associated with different types of errors.
• Competence with large-scale data handling and ML/AI platforms such as Snowflake, Apache Airflow for orchestration, and cloud environments (primarily AWS; GCP or Azure also acceptable).
• Strong communication abilities enabling alignment among technical and non-technical teams, transparent uncertainty discussion, and influence without direct authority.
• Dedication to quality above speedy delivery; a curious and rigorous approach to system performance and code comprehension.

Preferred Additional Experience

• Experience in graph theory, graph neural networks, knowledge graphs, or graph databases like Neo4j—which is highly valuable for this role.
• Background in software engineering including REST/gRPC services, Docker/Kubernetes, CI/CD pipelines, and feature store development.
• Experience with Snowflake advanced features like Snowpark and container services or similar warehouse-to-model deployment pathways.
• Involvement with building LLM evaluation frameworks, safety mechanisms, or operational tooling (LLMOps) for customer-facing AI.
• Exposure to fields such as risk, fraud, financial technology modeling, or large-scale recommendation and ranking systems.

Compensation and Benefits

This role offers a transparent salary range starting from $145,900 CAD to $197,400 CAD annually, reflecting progression from learning phase to expert level contributions. Most hires begin near the midpoint, with higher compensation awarded based on demonstrated expertise and impact.

Beyond base pay, the package includes equity incentives, annual health and wellness stipends, matched retirement savings, and comprehensive extended health coverage for physical and mental well-being.

Professional development is supported through dedicated talent coaching, career advancement programs, and a culture prioritizing growth.

What to Expect from Jobber

• A holistic compensation package with extended health benefits, retirement plan matching, and stock options.
• Access to talent development resources and leadership programs fostering career growth.
• An opportunity to influence a $400-billion industry undergoing transformation, with substantial room for innovation.
• A collaborative and caring work environment focused on customer success.

Equal Opportunity Statement

We value diversity and endeavor to create an inclusive workplace that welcomes varied experiences and perspectives. We are committed to equal opportunity employment and will provide accommodations throughout the hiring process upon request.