About the role
Description
Join our team
Eastern Field Operations (EFO) runs B2B field delivery across Quebec and Ontario — technicians, field managers — and we're building the AI-powered layer that makes that operation smarter. This is not an analytics role. You will build and ship tools that field managers and technicians actually use. You will own the data architecture that makes those tools possible. You will work directly with operations leadership to define the problems worth solving, and you will be accountable for getting working software into people's hands.If your instinct is to build rather than report, and you want your work to change how a large field team operates — this is that role.
What you'll do
Design and maintain the data architecture that underpins AI-powered field operations tooling — pipelines, semantic layer, and the clean interfaces that make operational data usable by LLMs and agents Build and deploy end-to-end tools: from raw telemetry and CRM data through to field manager-facing interfaces (dashboards, alerts, agentic workflows) Use agentic coding tools (Claude Code, Cursor, or equivalent) as part of your daily workflow — this role ships faster than traditional analyst roles because it builds differently Partner with data platform and BI teams to define where EFO data lives, how it gets modeled, and how it gets consumed — you own the field ops slice, not the whole warehouse Identify the 2–3 high-leverage use cases each quarter (coaching prep, schedule adherence, route deviation, performance/productivity metric visibility) and take them from concept to field-ready tool Instrument your own work: define success metrics, track field manager (FM) adoption, and present quarterly checkpoints to leadership Operate as an embedded IC — you have a seat at the director table, not a ticket queue
Qualifications
What you bring
Advanced SQL skills, including experience with BigQuery or equivalent cloud data platforms Proficiency in Python; comfort building scripts, pipelines, and lightweight APIs — not just notebooks Experience building data pipelines that serve operational (not just analytical) consumers Hands-on experience with agentic AI frameworks or LLM tooling (LangChain, Claude API, Vertex AI, or equivalent) — you've shipped something real with these tools, not just prototyped Strong data modeling fundamentals — you understand what a semantic layer is and why it matters Ability to work across ambiguous problem spaces: you can talk to a field manager, understand the operational friction, and translate it into a data question worth solving Comfort working without a large team — you build, own, and maintain your work Experience with source control (GitHub) and basic software development practices
Nice to have
Experience with operational or field-services data (dispatch, scheduling, telemetry, service orders) Familiarity with TELUS data platforms or BCX operational systems Experience building dashboards in Looker, Tableau, or equivalent BI tools Knowledge of MLOps practices: model versioning, deployment, monitoring Experience building ML models for regression, classification, or NLP tasks Bilingual English/French — asset for QC operational context
Advanced knowledge of English is required because you will most of the time interact in English with external parties (clients, suppliers, candidates, external partners, etc.); interact in English with internal parties (colleagues, internal partners, stakeholders, etc.); and work with IT tools whose interface is only accessible in English as part of this position's main responsibilities given its national scope.
Not the right fit? Search for Data Scientist jobs in Montreal, Quebec, Canada
Similar Jobs
About the role
Description
Join our team
Eastern Field Operations (EFO) runs B2B field delivery across Quebec and Ontario — technicians, field managers — and we're building the AI-powered layer that makes that operation smarter. This is not an analytics role. You will build and ship tools that field managers and technicians actually use. You will own the data architecture that makes those tools possible. You will work directly with operations leadership to define the problems worth solving, and you will be accountable for getting working software into people's hands.If your instinct is to build rather than report, and you want your work to change how a large field team operates — this is that role.
What you'll do
Design and maintain the data architecture that underpins AI-powered field operations tooling — pipelines, semantic layer, and the clean interfaces that make operational data usable by LLMs and agents Build and deploy end-to-end tools: from raw telemetry and CRM data through to field manager-facing interfaces (dashboards, alerts, agentic workflows) Use agentic coding tools (Claude Code, Cursor, or equivalent) as part of your daily workflow — this role ships faster than traditional analyst roles because it builds differently Partner with data platform and BI teams to define where EFO data lives, how it gets modeled, and how it gets consumed — you own the field ops slice, not the whole warehouse Identify the 2–3 high-leverage use cases each quarter (coaching prep, schedule adherence, route deviation, performance/productivity metric visibility) and take them from concept to field-ready tool Instrument your own work: define success metrics, track field manager (FM) adoption, and present quarterly checkpoints to leadership Operate as an embedded IC — you have a seat at the director table, not a ticket queue
Qualifications
What you bring
Advanced SQL skills, including experience with BigQuery or equivalent cloud data platforms Proficiency in Python; comfort building scripts, pipelines, and lightweight APIs — not just notebooks Experience building data pipelines that serve operational (not just analytical) consumers Hands-on experience with agentic AI frameworks or LLM tooling (LangChain, Claude API, Vertex AI, or equivalent) — you've shipped something real with these tools, not just prototyped Strong data modeling fundamentals — you understand what a semantic layer is and why it matters Ability to work across ambiguous problem spaces: you can talk to a field manager, understand the operational friction, and translate it into a data question worth solving Comfort working without a large team — you build, own, and maintain your work Experience with source control (GitHub) and basic software development practices
Nice to have
Experience with operational or field-services data (dispatch, scheduling, telemetry, service orders) Familiarity with TELUS data platforms or BCX operational systems Experience building dashboards in Looker, Tableau, or equivalent BI tools Knowledge of MLOps practices: model versioning, deployment, monitoring Experience building ML models for regression, classification, or NLP tasks Bilingual English/French — asset for QC operational context
Advanced knowledge of English is required because you will most of the time interact in English with external parties (clients, suppliers, candidates, external partners, etc.); interact in English with internal parties (colleagues, internal partners, stakeholders, etc.); and work with IT tools whose interface is only accessible in English as part of this position's main responsibilities given its national scope.
Not the right fit? Search for Data Scientist jobs in Montreal, Quebec, Canada