Staff Data Scientist, Applied Machine Learning

About Jobber and the Role

Join Jobber's expanding Data Science team as a Staff Data Scientist specializing in Applied Machine Learning. Jobber empowers small home service businesses such as plumbers, painters, and landscapers by providing them with technological tools to facilitate quoting, scheduling, invoicing, and payments, enhancing their customer experience. The industry is rapidly evolving, and Jobber aims to equip businesses with the flexibility to operate when and how they see fit.

The company culture prioritizes transparency, inclusivity, and innovation. Recognized among Canada's Top Growing Companies and backed by an executive team with decades of industry experience, Jobber offers extensive onboarding resources, mentoring opportunities, and supportive leadership focused on work-life harmony.

Team Structure

The Strategy and Analytics Department acts as a central hub that converts data into actionable insights, supporting various teams across the organization. The Data Science unit is key in predictive and prescriptive analytics, working in agile two-week sprints to create impactful models. The flagship initiative, SignalGraph, is a cross-company metric graph connecting organizational activities with outcomes to enhance decision-making.

Primary Responsibilities

• Enhance and maintain SignalGraph by improving metric-family contracts, ensuring segment and causal-edge integrity, managing the Neo4j and series-refresh pipelines, and developing diagnostic tools that help teams trace metric changes to root causes for faster decision-making.
• Design, implement, and evaluate retrieval-augmented generation (RAG) systems on top of the graph, taking responsibility for retrieval quality, context handling, and establishing evaluation frameworks to ensure system accuracy beyond surface fluency.
• Manage the full lifecycle of production ML models, including training pipelines, real-time serving, monitoring performance and drift, and implementing retraining procedures to ensure uptime, low latency, and robust accuracy.
• Introduce systematic evaluation and regression testing practices to maintain and defend the quality of models and large language model (LLM) systems over time rather than only at deployment.
• Set technical standards within the team, promoting MLOps and ML engineering best practices, contributing to the feature store and platform roadmap, reviewing colleagues’ work, and mentoring scientists on graph and deep learning methodologies.
• Collaborate closely with senior leadership and cross-functional stakeholders such as Customer Analytics, Business Intelligence, and Product teams, effectively communicating complex concepts, presenting work, and guiding partners towards data-driven decision-making aligned with Jobber’s key objectives.
• Stay at the forefront of AI/ML advancements, not only adopting new technologies but translating cutting-edge research into production capabilities.

Required Qualifications

• Demonstrated experience in deploying production ML models, encompassing training, deployment, serving, maintenance, and retraining to address issues such as drift and technical debt.
• Profound statistical knowledge allowing rigorous reasoning about bias, variance, and model loss functions, plus the ability to discern signal from noise with empirical justification.
• Expertise in SQL and Python geared for production-grade applications.
• In-depth familiarity with modern machine learning techniques, including deep learning, neural network architectures (transformers, BERT-family, RNN, CNN), ranking algorithms, representation learning, and practical deployment of LLMs such as RAG and context management systems.
• Experience designing asymmetric and custom loss functions informed by business impacts reflecting different costs of errors.
• Handling large-scale data in production environments, familiarity with platforms and orchestration tools like Snowflake, Apache Airflow, and cloud infrastructures primarily AWS (alternatively GCP or Azure).
• Strong interpersonal and communication skills to align technical and non-technical stakeholders, present uncertainty clearly, and influence decision-making without formal authority.
• Commitment to delivering quality solutions beyond speed, striving to deeply understand model performance and continuously improve it.

Preferred Qualifications

• Knowledge of graph theory, graph neural networks, knowledge graphs, or experience using graph databases such as Neo4j.
• Background in software engineering related to model serving, REST/gRPC interfaces, containerization with Docker/Kubernetes, CI/CD pipelines, and feature store development.
• Experience utilizing Snowflake’s Snowpark and container services or similar integration paths from data warehouses to deployed ML models.
• Familiarity with building infrastructure for evaluating LLMs, implementing safety layers, and LLM operations for customer-facing applications.
• Exposure to risk, fraud, fintech modeling, or large-scale recommendation and ranking systems.

Compensation and Benefits

The base annual salary range for this role is CAD 145,900 to CAD 197,400, reflecting growth from early-stage onboarding to expert-level performance. Compensation includes equity, annual wellness stipends, retirement savings matching, and a comprehensive health package with entirely paid premiums for physical and mental health. Professional growth is supported through a dedicated talent development team, coaching, and multiple career advancement programs.

Additional Information

• Jobber values diversity and inclusion and is committed to equal opportunity hiring practices.
• Accommodations are available throughout the hiring process on request.
• This role is based onsite in Edmonton, Alberta, Canada.

About Jobber's Mission

Jobber is revolutionizing service delivery for small businesses by connecting tens of thousands of service providers with their customers, helping them save time, invoice faster, and enhance payments. The company is a growing force aiming to reshape an industry valued at hundreds of billions of dollars annually.