Staff Data Scientist, Applied ML

Stack

Responsibilities

• ▹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.
• ▹Design, build and evaluate retrieval-augmented generation (RAG) systems on top of the graph; own retrieval quality, context management and the evaluation harness.
• ▹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.
• ▹Set the technical bar: drive MLOps and ML engineering standards, shape the feature store and platform roadmap, review peers' work and mentor other scientists on graph and deep learning methods.
• ▹Partner directly with senior leadership, Customer Analytics, Business Intelligence and Product; present your own work and defend your assumptions.
• ▹Stay current on developments in AI/ML methodology and translate them into shipped capability.

Requirements

• ▹Production ML experience, end-to-end: you have trained, deployed, served and maintained models that acted on real users, and learned the lessons about retraining, drift and technical debt.
• ▹A strong statistics foundation: reasoning about bias and variance, justifying a loss function, telling signal from noise.
• ▹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: building or tuning custom and asymmetric loss functions and explaining the business reasoning.
• ▹Experience with large data in production environments and the platforms that support it: ML/AI platforms such as Snowflake, orchestration with Apache Airflow, and cloud infrastructure (AWS strongly preferred; GCP or Azure equivalent considered).
• ▹Strong communication and stakeholder alignment skills: taking technical and non-technical partners from confusion to alignment, presenting uncertainty honestly, influencing without authority.
• ▹Ownership of quality over shipping speed alone; you do not outsource your understanding of your own code to an AI assistant.

Nice to have

• ▹Graph experience: graph theory, graph neural networks, knowledge graphs or graph databases such as Neo4j.
• ▹A software engineering background: services and model serving (REST/gRPC), Docker/Kubernetes, CI/CD, feature stores.
• ▹Experience with Snowflake, including Snowpark and Snowpark Container Services, or a comparable path from warehouse to deployed model.
• ▹Experience building LLM evaluation infrastructure, safety layers or LLMOps for customer-facing AI.
• ▹Exposure to risk, fraud or fintech modelling, or to recommendation and ranking systems at scale.

Soft skills

What we offer

• ▹Total compensation package including equity rewards and stock options
• ▹Extended health benefits package with fully paid premiums for body and mind
• ▹Matching in RRSP, TFSA or FHSA
• ▹Annual stipends for health and wellness
• ▹Dedicated talent development program with career coaching, learning and leadership programs

About the company

Jobber helps small home service businesses such as plumbers, painters and landscapers quote, schedule, invoice and get paid. A Canadian company recognized by Great Place to Work and others, it won its first customer in 2011.