Jobber is hiring a Staff Data Scientist Applied ML in Waterloo!
Jobber is hiring a Staff Data Scientist, Applied Machine Learning in Waterloo to own automated decisioning models and ML systems within its Data Science team. This is a senior individual-contributor role with architectural responsibility: the hire will shape how ML systems are built, evaluated, served, monitored, and trusted across Jobber’s product and analytics ecosystem. A central project is SignalGraph, a company-wide metric graph connecting organizational activity and outcomes, with the role also building retrieval-augmented generation systems on top of that graph and production models using SaaS event, scheduling, payments, and unstructured sales/support data. The position combines hands-on model development with MLOps standards, evaluation rigor, mentoring, and direct partnership with senior leaders.
Key responsibilities
- Maintain and improve SignalGraph, including metric-family contracts, segment and causal-edge catalogs, Neo4j and series-refresh pipelines, and investigation diagnostics.
- Design, build, and evaluate RAG systems on top of the metric graph, owning retrieval quality, context management, and evaluation harnesses.
- Own production ML end-to-end: training pipelines, real-time serving, monitoring, drift detection, and retraining.
- Establish systematic evaluation and regression testing for ML and LLM systems.
- Set MLOps and ML engineering standards, shape feature-store and platform direction, review peers’ work, and mentor scientists.
- Present work to senior leadership and cross-functional partners, defend assumptions, and align stakeholders on data meaning.
- Track AI/ML developments and translate them into shipped capabilities.
Key requirements
- End-to-end production ML experience, including deployed models that acted on real users and maintenance through retraining and drift.
- Strong statistics foundation and ability to justify modeling choices and separate signal from noise.
- Expert SQL and production-grade Python.
- Depth in deep learning, transformers/BERT-family models, RNN/CNN, ranking, representation learning, and LLMs in production including RAG.
- Experience with custom or asymmetric loss functions tied to business risk.
- Experience with large-scale data platforms, Snowflake, Apache Airflow orchestration, and AWS or equivalent cloud infrastructure.
- Strong communication, stakeholder alignment, and influence without authority.
- Commitment to quality, understanding why models work, and not outsourcing code comprehension to AI assistants.
- Seniority: Staff / senior individual contributor
- Work mode: Not specified
- Salary: Minimum annual salary $145,900 CAD; midpoint $171,600 CAD; maximum $197,400 CAD
- Key skills: Python, SQL, Deep learning, Transformers, LLMs, RAG, Ranking, Representation learning, MLOps, Snowflake, Apache Airflow, AWS, Neo4j, Graph ML
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