Senior Data Scientist

Location: Victoria

Position Description
We are seeking a versatile Senior Data Scientist who can work across applied analytics and machine learning domains, including computer vision, statistical analysis, and policy‑relevant insight generation. This role is ideal for a data scientist who enjoys balancing exploratory analysis, model development, and clear communication of findings to technical and non‑technical audiences.

You will work with multidisciplinary teams including data engineers, BI developers, architects, and business stakeholders. You will focus on analysis, modeling, experimentation, and insight generation. This position supports a wide range of analytics and AI initiatives such as computer vision prototyping, urban and regulatory analytics, housing and economic trend analysis, and other data‑driven decision‑support projects.

Your future duties and responsibilities

Conduct statistical, time‑series, and trend analysis to identify patterns, anomalies, changes over time, and emerging signals.

Develop clear, reproducible analytical methods, KPIs, and metrics used in dashboards and recurring reports.

Translate analytical outcomes into recommendations, narratives, and visual summaries for stakeholders.

Support compliance, policy, planning, operational, or performance‑monitoring use cases with data‑driven insights.

Collaborate with BI developers to ensure analytical logic is accurately reflected in dashboards and reports.

Computer Vision & Machine Learning

Design and run computer vision experiments (classification, detection, segmentation) using real‑world imagery.

Handle preprocessing challenges such as variable image quality, occlusions, inconsistent lighting, and diverse viewpoints.

Develop evaluation methods, dataset documentation, and experiment reproducibility practices.

Work within modern ML platforms to manage models, experiment tracking, and model artifacts.

Participate in human‑in‑the‑loop evaluation workflows to validate model performance and support iterative improvement.

Collaboration & Governance

Work within Azure ML and related Azure services.

Partner with data engineers, architects, and SMEs to ensure analytical readiness and data quality.

Produce well‑structured documentation: model cards, metric definitions, analytical notes, and reproducible notebooks.

Adhere to privacy, security, and Responsible AI practices when handling sensitive or identifiable data.

Communicate results clearly to diverse technical and non‑technical audiences.

Required Qualifications To Be Successful In This Role

Bachelor's degree in a quantitative or technical discipline is required.

Strong analytical reasoning skills, including the ability to translate ambiguous business or policy questions into structured analytical or ML approaches.

Experience applying modern AI techniques (e.g., embeddings, semantic search, or foundation‑model‑powered analysis) in practical, production‑minded settings.

Hands‑on experience evaluating or integrating pre‑trained models into analytics or ML workflows using Azure AI services.

Experience with Azure AI Foundry or similar foundation‑model experimentation environments.

Demonstrated ability to collaborate effectively with data engineers, BI developers, architects, and domain experts in multidisciplinary teams.

Experience designing and evaluating experiments, including defining metrics, baselines, sampling strategies, and reproducible evaluation procedures.

Understanding of Responsible AI principles, risk mitigation, bias evaluation, and explainability best practices.

5+ years of experience in applied data science or analytics, producing measurable business or policy impacts.

Strong Python skills (pandas, numpy, scikit‑learn; plus PyTorch or Tensor Flow for ML/CV).

Strong SQL skills for querying and analytical exploration.

Ability to communicate complex technical concepts in clear, accessible language.

Experience working in environments with privacy, security, or auditability requirements.

Preferred Qualifications

Master's degree in Data Science, Computer Science, Engineering, Statistics, or a related field is preferred.

Experience with cloud‑based ML environments or ML lifecycle tools.

Experience with geospatial data,…