Sr Data Scientist

Job Title: Senior Data Scientist
Location: Toronto, ON – 3 days onsite (airport)
Position Type: Expected 6-month contract

Senior Data Scientist role within our client’s Advanced Analytics team. This posting is for two headcount and both are end-to-end data science roles and are interchangeable by design.

Role Summary:
The Advanced Analytics team is building a trusted, machine-learning-driven capacity-planning capability in-house. The Senior Data Scientist designs, builds, and validates predictive and decision-support models for airport operations. Each person takes a problem from raw operational data through validated models to outputs that operational and business stakeholders can trust and act on. Two scientists are being hired to provide the team with depth, resilience, and the capacity to scale across workstreams.
This is a hands-on ownership role. The priority is turning complex, imperfect operational data into accurate, explainable, and supportable analytics. Accuracy and explainability are the standard, not optional extras.

Details:
Scope of Ownership: Senior Data Scientist owns analytical workstreams end to end. The following areas describe the scope of ownership for each role.

Data foundation
Validate and prepare operational data; establish trusted, analytics-ready datasets.
Forecasting & modelling
Build demand forecasts and predictive models using statistical, time-series, and machine learning methods.
Optimization & decision support
Translate predictions into capacity, risk, and recommendation outputs that inform operational decisions.
Validation
Define and run validation that reflects real operational conditions, not only average-case accuracy.
Production readiness
Package models for deployment and monitoring in partnership with ML engineering.
Stakeholder Trust
Communicate methods, assumptions, and limitations clearly to technical and non-technical audiences.

Full-Stack Expectation:
Each role spans the full analytics lifecycle. The successful candidate is comfortable across the following areas:

1. Data discovery and validation — profiling, quality checks, and source reconciliation.
2. Modelling — statistical, time-series, and machine learning approaches, with strong baselines before advanced methods.
3. Optimization and decision support — converting model outputs into actionable, constraint-aware recommendations.
4. Productionization — collaborating with MLOps and ML engineering to deploy, monitor, and maintain models.
5. Business consumption — partnering with business intelligence and operational stakeholders to make outputs usable and trusted.

Key Responsibilities:
The Senior Data Scientist is expected to perform the following responsibilities:

1. Own analytical workstreams end to end, from data to validated, decision-ready outputs.
2. Validate operational data and define clear, defensible data foundations before modelling.
3. Build transparent baselines before advanced models and justify any added complexity.
4. Apply statistical, time-series, machine learning, and optimization methods as appropriate.
5. Validate models against real operational conditions and communicate performance honestly.
6. Translate model outputs into recommendations stakeholders can act on with confidence.
7. Partner with Data Engineering, Business Intelligence, Operations, and MLOps / ML engineering.
8. Produce production-readiness artifacts and support clean handoff to deployment and monitoring.
9. Communicate uncertainty and limitations clearly, and identify when an output is not ready for operational use.

The following experience is considered an asset:

1. Airport, airline, transit, logistics, or other complex operations experience.
2. Forecasting, queueing, simulation, or optimization methods.
3. Microsoft Fabric, Power BI, Azure Machine Learning (Azure ML), MLflow, and Git.
4. Model governance: versioning, validation, monitoring, and retraining practices.
5. Experience designing decision-support or recommendation outputs.