Data Scientist
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- Markham, ON
- 100.000-140.000 $ par an
- Permanent
- Temps-plein
- Gather business requirements and frame data science use cases with measurable success criteria
- Perform data discovery, profiling, cleaning, and feature engineering across large, complex datasets
- Build, evaluate, and iterate on supervised and unsupervised ML models (e.g., classification, regression, clustering)
- Implement robust experimentation, validation, and statistical testing to ensure model quality and reliability
- Collaborate with data engineers to design scalable ETL/ELT and model serving pipelines
- Optimize code and queries for performance, cost, and maintainability in cloud environments
- Create clear, executive-ready narratives and visualizations to communicate insights and recommendations
- Contribute to productionization with MLOps teams (packaging, CI/CD, monitoring, model drift detection)
- Follow engineering best practices: version control, code reviews, unit testing, documentation
- Stay current on ML/AI techniques and bring practical innovations into client projects
- Bachelor’s degree in quantitative field (Computer Science, Engineering, Mathematics, Statistics, or related)
- 4+ years of hands-on experience building and deploying ML models end-to-end (problem framing → data prep → modeling → validation → documentation → deployment)
- Proficiency in Python (Pandas, NumPy, scikit-learn) and strong SQL for data manipulation and performance tuning
- Solid understanding of data modeling, feature engineering, and evaluation metrics for supervised/unsupervised learning
- Experience working with large datasets and modern data ecosystems (cloud data warehouses, files/object storage)
- Ability to communicate complex analytical findings to technical and non-technical audiences and drive stakeholder alignment
- Experience with big-data and distributed computing frameworks (e.g., Spark, PySpark) and/or Databricks
- Exposure to cloud analytics services (AWS, Azure, or GCP) and orchestration (e.g., Airflow, ADF, Glue)
- Familiarity with MLOps practices (model packaging, CI/CD, observability, drift monitoring)
- Experience in consulting or client-facing roles, translating business needs into technical deliverables
- Experience with NLP, time-series, or recommendation systems
- Knowledge of experiment design, causal inference, and uplift modeling
- Familiarity with BI/visualization tools (e.g., Power BI) and storytelling techniques
- Exposure to SAS (e.g., SAS Viya) for analytics in regulated environments
- Opportunity for advancement and career progression
- Competitive compensation package
- Comprehensive benefits plan
- Successful referral program
- The opportunity to work with one of Canada’s 50 Best Managed Companies
- Satisfaction of working for a reputable company
- A flexible, dynamic, and diverse workplace