Data Scientist, Business Intelligence & Reporting

Remote (Canada)

Job Details

Location: Canada

Workplace: Remote

Employment Type: Full Time

Experience: Minimum 2 years of experience in statistical analysis, data science, or advanced analytics

Core Areas: Statistical Modeling, Machine Learning, Python, SQL, Experimental Design, Time Series Forecasting, GenAI/LLMs, Model Validation & Monitoring

Schedule: 40 hours per week; occasional extra or flexible hours may be required

Compensation: $70,000–$85,000 annual base salary

About the Role

The Data Scientist, Business Intelligence & Reporting - Canada Remote designs and delivers statistical, machine learning, and AI solutions that translate business problems into measurable improvements. The role owns scoped analytical problems through solution development, validation, and delivery, with responsibilities spanning predictive modeling, experimentation, time series forecasting, anomaly detection, and production-ready analytical workflows.

This position requires strong Python and SQL capabilities, statistical inference, supervised and unsupervised machine learning, experiment design, and practical application of GenAI and language models. The role also works with business stakeholders, Data Engineering, IT Infrastructure, and Data Governance to turn analytical findings into reliable solutions, metrics, reporting, and production pipelines.

What You'll Do

• Design and implement supervised learning models for classification and regression, including feature engineering, feature selection, model tuning, and evaluation.
• Apply clustering, segmentation, and anomaly detection techniques to identify meaningful patterns and unusual behavior in data.
• Apply language models to classification, extraction, intelligent document processing, and other problems where they outperform conventional approaches.
• Support and extend time series forecasting solutions.
• Design sampling approaches, experiments, and hypothesis tests to evaluate business questions and quantify impact.

• Work with the responsible manager to scope analytical problems and take them through solution development, validation, and delivery.
• Develop production-quality, reusable Python code and frameworks for data preparation, model training, and model evaluation.
• Develop data models that support analytics and internal products.
• Make validation and monitoring part of solution delivery by establishing baselines, performing backtesting, evaluating error metrics, and detecting model drift for solutions in ongoing use.
• Document analytical work and maintain version control so another person can run, audit, maintain, and extend it.
• Work with Data Engineering and IT Infrastructure on data access, deployment, and the transition of validated solutions into production pipelines.

• 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 analyses that answer specific business questions.
• Translate analytical findings into metrics and reporting that business teams can use for decision-making.

• Work with business stakeholders to translate business questions into well-defined analytical problems.
• Present complex statistical concepts and analytical insights through clear storytelling, visualization, and business-focused recommendations.
• Take ownership of work through delivery, identifying risks and unresolved questions early.
• Support solution handoffs through documentation and walkthroughs for the teams responsible for operating delivered solutions.
• Work with Data Governance to improve the quality and definitions of data used for analytics and modeling.

• Identify business processes where new analytical, statistical, machine learning, or AI methods could improve efficiency, accuracy, or decision quality, and prototype approaches to evaluate their effectiveness.
• Run experiments with success criteria defined in advance and clearly report the results.
• Stay current with emerging statistical, machine learning, and AI methods and evaluate their practical fit for business problems.

Qualifications

Required Experience

• Minimum of 2 years of experience in statistical analysis, data science, or advanced analytics.
• Experience building, validating, and diagnosing supervised and unsupervised learning models, including classification, regression, and clustering, for business problems.
• Hands-on experience applying GenAI or large language models to practical problems.
• Exposure to time series forecasting methods.
• Experience working with cloud data warehouses and business intelligence platforms.
• Experience documenting and structuring analytical work so others can reproduce, maintain, and extend it.

Required Skills

• Proficiency in SQL, Python, and Git for analytics, statistical modeling, machine learning, and version-controlled analytical development.
• Solid understanding of statistical inference, hypothesis testing, experimental design, and analytical validation.
• Ability to develop and evaluate supervised and unsupervised machine learning models using appropriate feature engineering, model selection, tuning, and diagnostic techniques.
• Ability to translate business needs and business questions into analytical problems and practical data-driven solutions.
• Strong analytical and critical-thinking abilities with a high level of attention to detail and analytical accuracy.
• Strong communication skills with the ability to explain complex statistical and analytical concepts to diverse audiences.
• Ability to communicate findings through clear storytelling, visualization, metrics, reporting, and business-focused recommendations.
• Ability to independently manage multiple projects and follow work through to a delivered outcome.

Education

• Bachelor's degree in Computer Science, Data Science, Statistics, Economics, or a related quantitative field.

Preferred Qualifications

• Hands-on experience with AWS Redshift.
• Hands-on experience with Power BI, Tableau, or Looker.
• Experience working with ERP source data, particularly SAP.
• Experience in the recycling industry or another regulated, reporting-heavy environment such as extended producer responsibility, utilities, public sector, or finance.
• Agile and sprint development experience.

Working Conditions

• Candidates must reside in Canada.
• The position is performed remotely from a home office environment.
• The standard work schedule is 40 hours per week.
• Extra or flexible hours may occasionally be required.
• Occasional in-person meetings may be required.
• Artificial intelligence is not used during the hiring process.
• Only applicants under consideration will be contacted.
• Applicants who complete an interview will be informed whether a hiring decision has been made within 45 days of their final interview.