Data Scientist, Business Intelligence & Reporting - Canada Remote

Circular Materials

5d•Remote

About The Position

Reporting to the Manager, Data Science, the Data Scientist designs and delivers statistical, machine learning, and AI solutions that turn business problems into measurable improvements. The Data Science team is small and project-based that supports business processes across the organization while increasingly leaning into experimentation: prototyping new methods, validating whether they work, and handing proven solutions to the teams that will run them. The role favours breadth over specialization and will help shape the team’s methods and foundations as it grows. To thrive in this role, the Data Scientist must be fluent in turning data into decisions: proficient in Python and SQL; versed in predictive modeling, statistical inference, and time series; familiar with modern ML and analytics workflows (feature engineering, validation, experiment design, and monitoring); and comfortable selecting the right tool for the problem, whether that is a heuristic, a classical model, or a language model. The role requires sharp attention to detail and a commitment to reproducibility through clear documentation, version control, and repeatable code. This is a full-time, salary paid position, which requires residency in Canada.

Requirements

Bachelor’s degree in Computer Science, Data Science, Statistics, Economics or a related quantitative field.
Minimum of 2 years of experience in statistical analysis, data science, or advanced analytics
Proficient in SQL, Python, and Git for analytics, statistical modeling, and machine learning
Experience building, validating, and diagnosing supervised and unsupervised learning models (classification, regression, clustering) for business problems
Solid understanding of statistical inference, hypothesis testing, and experimental design
Hands-on experience applying GenAI or LLMs to practical problems
Exposure to time series forecasting methods
Experience working with cloud data warehouses and BI platforms
Experience documenting and structuring analytical work so that others can reproduce and maintain it

Nice To Haves

Hands-on experience with AWS Redshift
Hands-on experience with Power BI, Tableau, or Looker
Experience working with ERP source data, particularly SAP
Prior working experience in the recycling industry, or in regulated and reporting-heavy environments (EPR, utilities, public sector, finance)
Agile and sprint development experience

Responsibilities

Statistical Modeling and Machine Learning: Design and implement supervised learning models for classification and regression, including feature engineering, selection, and tuning. Apply clustering, segmentation and anomaly detection techniques to identify patterns and unusual behaviour in data. Apply language models to problems such as classification, extraction, and intelligent document processing where they outperform conventional methods. Support and extend the team's time series forecasting work. Design sampling approaches, experiments and hypothesis tests to evaluate business questions and quantify impact.
Solution Development and Delivery: Take scoped problems through to delivered solutions. Develop production-quality, reusable Python code and frameworks for data preparation, model training, and evaluation. Develop data models supporting analytics and internal products. Treat validation and monitoring as part of delivery, including baselines, backtesting, error metrics, and drift detection for models in ongoing use. Document work and maintain version control to a standard that lets another person run, audit, and extend it. Work with Data Engineering and IT Infrastructure on data access, deployment, and the transition of validated work into production pipelines.
Data Analysis and Reporting: Source data for analysis and experimentation from curated warehouse tables, enterprise source systems, APIs, flat files, and offline sources including Excel workbooks. Produce ad-hoc and recurring analysis that answers specific business questions. Translate analytical findings into the metrics and reporting business teams use to make decisions.
Collaboration and Communication: Work with business stakeholders to turn business questions into analytical problems. Present complex statistical concepts and insights through clear storytelling, visualization, and business-focused recommendations. Act like an owner by following work through to a delivered outcome, surfacing risks and open questions early rather than waiting to be asked. Support the handover of delivered solutions, including documentation and walkthroughs for the teams that will operate them. Work with Data Governance to improve the quality and definitions of the data the team relies on.
Continuous Improvement: Identify business processes where a new method could improve efficiency, accuracy, or decision quality, and prototype it to show whether it works. Run experiments with success criteria defined up front, and report clearly on results. Stay current with emerging statistical, machine learning, and AI methods and assess them for practical fit.