Jobber is hiring a Staff Data Scientist Applied ML in Vancouver!
Jobber is hiring a Staff Data Scientist, Applied Machine Learning in Vancouver to take senior individual-contributor ownership of production ML systems and the company's SignalGraph metric graph. The role blends deep technical work with commercial judgment: building models that support automated decisioning, designing retrieval-augmented generation over governed graph evidence, and ensuring live systems remain reliable, measurable, and trusted. This is an architectural and standard-setting position rather than a ticket-based execution role. The successful candidate will work with high-volume SaaS, scheduling, payments, and unstructured support data, partner with senior leaders, and raise the team's bar for evaluation, MLOps, and applied deep learning. Jobber values transparent compensation, professional growth, and an inclusive culture.
Key responsibilities
- Maintain and improve SignalGraph, including metric-family contracts, segment and causal-edge catalogs, Neo4j operations, refresh pipelines, and investigation diagnostics.
- Design, build, and evaluate RAG systems on top of the graph, owning retrieval quality, context management, and evaluation harnesses.
- Own production ML end to end: training pipelines, real-time serving, monitoring, drift detection, retraining, uptime, latency, and accuracy.
- Establish systematic evaluation and regression testing for models and LLM systems so quality is measured continuously.
- 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 technical and non-technical stakeholders.
- 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 lessons in retraining, drift, and technical debt.
- Strong statistics foundation for reasoning about bias and variance, loss functions, and signal versus noise.
- Expert SQL and production-grade Python.
- Depth in modern ML methods: deep learning, transformers/BERT-family models, RNN/CNN, ranking, representation learning, and LLMs in production including RAG.
- Experience with custom or asymmetric loss functions and the business reasoning behind them.
- Experience with large-scale production data platforms, Apache Airflow orchestration, and cloud infrastructure, with AWS preferred.
- Strong communication, stakeholder alignment, and influence without authority.
- Commitment to quality and deep understanding over shipping speed alone.
- Seniority: Staff / senior individual contributor
- Work mode: Not specified in listing; Vancouver location
- Salary: CAD $145,900–$197,400 annually (minimum $145,900; midpoint $171,600; maximum $197,400)
- Key skills: Python, SQL, Deep learning, Transformers/BERT, LLMs/RAG, Ranking, Representation learning, MLOps, Apache Airflow, Snowflake, AWS, Graph learning, Neo4j, Statistics, Stakeholder communication
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