Senior Data Scientist

Team: IT

Location: Canada

Commitment: Full-time

Workplace Type: remote

Accountabilities:

Design, develop, and continuously improve sales forecasting models across multiple product lines, business units, and geographic regions.
Build statistical and machine learning solutions for both short-term and long-term revenue forecasting, ensuring accuracy, scalability, and reliability.
Own the complete machine learning lifecycle, including data preparation, feature engineering, model training, evaluation, deployment, monitoring, and explainability.
Apply advanced forecasting methodologies such as ARIMA, Prophet, LSTM, and other relevant time-series modeling techniques to solve complex business challenges.
Develop self-service forecasting tools and scalable analytical solutions that enable business teams to access insights and make informed decisions in real time.
Translate model outputs, statistical findings, and predictive insights into clear business recommendations and strategic planning opportunities.
Collaborate closely with analytics engineers, data analysts, and business stakeholders to align forecasting solutions with organizational priorities.
Contribute to forecasting strategy and influence data-driven planning processes across the organization.
Mentor junior data scientists, participate in technical reviews, and help establish best practices, coding standards, and machine learning methodologies.
Continuously improve model performance, analytical processes, and production workflows through experimentation and innovation.
Requirements:
At least 5 years of professional experience in data science, machine learning, quantitative analytics, or a related field.
Strong background in sales forecasting, revenue analytics, and time-series modeling, with proven experience developing predictive solutions for business planning.
Expertise in forecasting methodologies such as ARIMA, Prophet, LSTM, or equivalent statistical and machine learning approaches.
Advanced proficiency in Python or R for data analysis, statistical modeling, and machine learning development.
Strong SQL skills, with experience querying, transforming, and analyzing complex datasets for modeling and reporting purposes.
Hands-on experience training, evaluating, and optimizing machine learning models using frameworks such as PyTorch, scikit-learn, XGBoost, or equivalent technologies.
Proven experience deploying machine learning systems into production environments using platforms such as AWS SageMaker, Databricks, or Snowflake.
Solid understanding of software engineering best practices, including version control, automated testing, code quality, and reproducible workflows.
Excellent analytical and problem-solving abilities, with the capacity to translate complex technical findings into practical business insights.
Strong communication and presentation skills, particularly when explaining technical concepts and recommendations to non-technical stakeholders.
Ability to work independently, manage priorities, and collaborate effectively within highly cross-functional teams.
Demonstrated leadership potential and willingness to mentor colleagues and contribute to team-wide technical standards.
Nice to Have:
Hands-on experience working with Snowflake and modern cloud-based data platforms.
Familiarity with Docker, Kubernetes, and containerized machine learning deployments.
Previous experience within education technology or organizations focused on educational products and services.
Experience building and scaling forecasting systems across multiple business units, product portfolios, or geographic markets.
Benefits:
Competitive annual base salary ranging from USD 110,000 to USD 150,000, depending on experience and qualifications.
Eligibility for an annual discretionary performance-based bonus.
Comprehensive medical, dental, and vision insurance coverage.
401(k) retirement savings plan.
Mental health and wellness benefits.
Paid time off and parental leave.
Professional development opportunities and learning programs.
Fully remote working environment.
Opportunity to make a meaningful impact on educational products and learning outcomes at scale.
Exposure to advanced data science, machine learning, and predictive analytics initiatives.
Opportunity to influence strategic business decisions and contribute to the evolution of forecasting capabilities.