Senior Data Developer, Scheduling & Forecasting; ServiceOS

Position: Senior Data Developer, Scheduling & Forecasting (ServiceOS)
About Dialogue
Dialogue is the #1 virtual care provider in Canada. By developing our Integrated Health Platform, we provide exceptional online health and wellness programs (primary care, mental health, iCBT, EAP, and wellness) to organizations that want to improve the wellness of their employees and families.

When it comes to our work, we set the bar high. Together, we’re transforming health and helping millions improve their well‑being. We’re firm believers that great people don’t settle on:

Impact
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Feel like you can make a difference? Good news, we saved you a seat!

Equal‑Opportunity Employment
Come as you are. As a proud equal‑opportunity employer, Dialogue is dedicated to creating a diverse and inclusive workplace for everyone. Qualified applicants will be considered regardless of citizenship, ethnicity, race, colour, religion, gender, gender identity or expression, sexual orientation, disability, age, or veteran status. Applicants who require specialized accommodation are encouraged to contact

AI Disclosure Statement
To ensure an efficient and fair review process, we utilize artificial intelligence tools to assist in the initial screening and assessment of applicants for this role.

Recruitment Fraud Notice
Dialogue only contacts candidates through official  email addresses. We never request payment, gift cards, or personal financial information at any stage of hiring, and we never extend an offer without a structured interview process. All open roles are listed exclusively at  If you receive a suspicious communication claiming to be from Dialogue, do not respond — report it to  and verify the role on our official careers page.

Your role as Senior Data Developer
We are seeking a Senior Data Developer to bring intelligence, automation, and resilience to Dialogue’s scheduling and appointment‑booking ecosystem. You will lead the technical implementation of our next‑generation forecasting, continuous‑learning, and optimization pipelines—powering the systems that match patients with the right clinician across every Canadian jurisdiction.

This role sits at the intersection of data engineering, applied ML, and backend production. You will be responsible for building the foundations that allow our team to continuously re‑train ML models, iterate quickly on forecasting and optimization logic, and ship reliable inference flows that support real‑time decision‑making.

About the Scheduling and Forecasting Team
As Canada's leading virtual care provider, Dialogue is committed to helping millions of individuals improve their health and well‑being. The Scheduling & Forecasting team powers the operational backbone that makes this possible: we build and run the systems that schedule our healthcare providers, forecast demand, and match patients with the right clinician across every Canadian jurisdiction. Our work directly influences SLAs, occupancy rates, provider workload balance, and the reduction of manual administrative effort across the company.

What you'll be doing
As a Senior Data Developer you will serve as the glue between backend engineering, data science, and operations—ensuring that forecasting models, optimization solvers, and data‑driven decision flows are not just theoretically correct, but dependable and observable in production.

You will design and maintain the pipelines, data architectures, and experimentation tooling that enable rapid iteration, high reliability, and continuous improvement of our scheduling intelligence.

Day‑to‑day you will:

Build and maintain continuous‑learning pipelines, automated training pipelines for ML forecasting models, incorporating new data and guardrails for performance drift.

Create reproducible CI workflows for both ML models and CP‑SAT solver configurations, including automated regression checks and benchmarking.

Develop and own real‑time and batch inference pipelines that power appointment‑demand forecasting and provider‑scheduling decisions.

Implement robust observability—structured logging, metrics, tracing, and alerting—to ensure models and solvers behave as expected in production.

Enable a tight research loop by designing tooling and data flows…