Remote Quantitative Developer Intern Jobs in Ontario

As a Data Scientist II, you will leverage theory, data, and research to solve business problems. You will support data science and analytics efforts across multiple areas of the business including Sales, Marketing, Finance, HR, and related functions. You will contribute to building and improving measurement and reporting processes. To this end, you will help teams access insights needed to operate effectively.

This role will be responsible for drawing insights from large data sets, defining and implementing key model performance indicators, and for communicating insights and trends to support business decision-making, as it relates to data science-enabled decisions. This role will work with datasets relevant to assigned projects and business areas and be responsible to work closely with business stakeholders on measurement, success metrics, and analytics. Effectiveness in this position will require an understanding of technical methods and data engineering necessary to build and implement data science models, as well as knowing general industry trends, business objectives, and workforce dynamics. You will use data to develop insights, forecasts, metrics, dashboards and recommendations to inform decisions about our operations and go-to-market strategy.

• Contribute to data science projects supporting Sales, Marketing, Finance, HR, and related functions, collaborating with other team members.
• Apply an understanding of business operations to translate defined requirements into data science tasks and KPIs, and identify opportunities where data science can support team objectives.
• Translate and summarize data into written reports, tables, graphs, dashboards, and charts to convey findings to the team and immediate stakeholders.
• Perform data preprocessing, feature engineering, and model selection for routine problems, working independently on well-defined or ambiguous tasks.
• Design and implement AI models and pipelines based on documented requirements, and analyze model performance using standard evaluation metrics.
• Use distributed processing systems (e.g., Snowflake, Databricks, Google Cloud Platform) to handle datasets of increasing size and complexity.
• Proactively identify and resolve issues in pipeline development and deployment.
• Write understandable, modular code by applying established software development practices and style guides.
• Use common data science libraries to implement designed solutions efficiently.
• Participate in code reviews at critical points to validate that code meets requirements and standards, and create initial technical documentation.

The information in this job description represents a summary of the role and is not intended to be a comprehensive list of job duties. Responsibilities and duties of the position may change without notice at the Company's discretion.

• Master's degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related).
• 2+ years of professional experience building and deploying data science or machine learning solutions, including at least one production deployment.
• Demonstrated experience translating business questions into data science problems and communicating findings to technical and non-technical audiences.
• Proficiency in Python and SQL, and experience with data visualization tools such as Tableau.
• Experience working with cloud data and ML platforms such as Snowflake, Databricks, or Google Cloud Platform.
• Working knowledge of applied statistical methods and machine learning techniques (e.g., regression, classification, time series, cross-validation, model evaluation).

• Doctoral degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related).
• Familiarity with MLOps practices - model versioning, monitoring, drift detection, CI/CD for ML.
• Experience designing and maintaining dashboards for operational or executive audiences.
• Experience presenting analyses to senior technical and non-technical audiences.
• Exposure to Sales, Marketing, Finance, or HR analytics domains.

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