Data Science – Canada | Remote Opportunity

We are hiring on behalf of one of our clients for a Data Science opportunity focused on developing intelligent, data-driven solutions, predictive models, and machine learning applications.

Job Title: Data Scientist

Location: Canada | Remote

Role Overview The successful candidate will work with structured and unstructured data to uncover meaningful patterns, develop predictive solutions, and support data-driven business strategies. This opportunity involves working across the complete data science lifecycle, from data exploration and feature development to model evaluation and deployment.

Key Responsibilities

- Explore large and diverse datasets to identify trends, relationships, anomalies, and potential predictive signals.
- Define analytical approaches for business and technical problems using statistical and machine learning techniques.
- Develop, train, test, and optimize supervised and unsupervised machine learning models.
- Perform feature engineering, feature selection, and data transformation to improve model performance.
- Design experiments and evaluate different modeling approaches using appropriate statistical methodologies.
- Build predictive models for areas such as customer behavior, demand forecasting, risk assessment, classification, and business optimization.
- Conduct exploratory data analysis to understand data quality, distributions, correlations, and underlying patterns.
- Develop reusable data science workflows and analytical processes using Python or similar programming languages.
- Evaluate models using appropriate performance metrics and conduct error analysis to identify opportunities for improvement.
- Apply statistical techniques to validate assumptions and measure the reliability of analytical findings.




- Work with data engineers and technical teams to prepare datasets and improve data pipelines for machine learning applications.
- Assist in deploying analytical models into production environments and monitor their performance over time.
- Identify model drift, data inconsistencies, and other factors that may affect the reliability of deployed solutions.
- Document data preparation methods, modeling approaches, assumptions, experiments, and results.
- Communicate technical findings and model outputs to business stakeholders in a clear and understandable manner.
- Translate business requirements into measurable data science objectives and analytical solutions.
- Collaborate with product, engineering, business, and analytics teams on data-driven initiatives.
- Research emerging machine learning techniques and assess their potential application to business problems.
- Contribute to improving existing models, analytical frameworks, and data science processes.

Required Skills

- Strong foundation in data science, machine learning, statistics, and data analysis.
- Proficiency in Python and commonly used data science libraries.
- Experience working with libraries such as Pandas, NumPy, Scikit-learn, or similar tools.
- Strong understanding of supervised and unsupervised learning concepts.
- Ability to work with SQL and relational databases.
- Understanding of model validation, performance metrics, and statistical evaluation.




- Strong problem-solving and analytical thinking skills.
- Ability to work with large datasets and handle data preparation challenges.
- Good communication skills with the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills

- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Knowledge of natural language processing, time-series analysis, or recommendation systems.
- Exposure to generative AI, large language models, or AI-powered applications.
- Familiarity with cloud-based machine learning environments.
- Understanding of MLOps concepts, model deployment, monitoring, and version control.
- Experience with tools such as Git, Jupyter, MLflow, or similar platforms.
- Knowledge of data visualization tools such as Power BI, Tableau, or Python visualization libraries.
- Familiarity with statistical experimentation, A/B testing, and hypothesis testing.
- Exposure to APIs, model serving, or integrating machine learning models into applications.
- Experience working with real-world data science projects or research-based analytical work.

What We Offer

- Remote chance supporting data science initiatives in Canada.
- Opportunity to work on practical machine learning and predictive analytics projects.
- Exposure to the complete data science lifecycle, from data preparation to model deployment.
- Opportunity to work with modern AI, machine learning, and analytical technologies.
- Experience collaborating with multidisciplinary technical and business teams.
- Opportunities to develop expertise across different data science use cases.
- Professional growth through hands-on projects involving real-world datasets.
- Opportunity to strengthen your data science portfolio with practical project experience.

📌 Data Science Intern (Canada)
🏢 Stealth Startup
📍 Canada