Machine Learning Engineer
- Ontario
- Permanent
- Full-time
- Design, build, and deploy machine learning models that power growth initiatives, such as customer segmentation, churn prediction, personalization, campaign optimization, and recommendation systems.
- Collaborate with data scientists to translate prototypes into scalable solutions.
- Collaborate with analysts and product managers to turn business questions into measurable ML solutions.
- Evaluate and select appropriate algorithms and models for specific tasks, ensuring scalability and efficiency.
- Develop and maintain data pipelines for model training, validation, and deployment.
- Develop scalable data and ML pipelines using best-in-class tools and practices (e.g., Airflow, Spark, MLflow).
- Conduct model testing, versioning, and documentation to ensure reproducibility and maintainability.
- Integrate ML models into product and marketing systems via APIs or batch/streaming services.
- Monitor model performance in production and implement feedback loops for continuous learning.
- Contribute to experimentation frameworks (e.g., A/B testing infrastructure) to evaluate ML-driven features.
- Ensure best practices in model validation, testing, and performance evaluation.
- Continuously improve existing systems by integrating new data sources and ML techniques.
- Maintain documentation, testing, and governance around models and datasets to ensure reliability and transparency.
- Work closely with stakeholders across various departments (e.g., Marketing, Sales, Product, R&D) to understand business needs and translate them into data science and machine learning solutions.
- Communicate complex technical concepts to non-technical stakeholders clearly and effectively.
- Stay up-to-date with the latest trends and advancements in machine learning and AI, and integrate new techniques into the team's workflow.
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or related field.
- 2+ years of experience deploying ML models in production environments.
- Proficient in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch, or LightGBM and tools for model deployment (e.g., MLflow, Kubernetes, Docker, Metaflow)..
- Experience with data pipeline tools (e.g., Airflow, dbt) and big data processing (e.g., Spark, Presto).
- Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Google Vertex AI).
- Proficient in programming languages such as Python, R, or Java.
- Solid understanding of statistical methods, machine learning algorithms, and deep learning techniques.
- Proven experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, GCP, Azure).
- Strong understanding of experimentation design and metrics relevant to growth (e.g., conversion rate, LTV).
- Comfortable working in a fast-paced, collaborative environment focused on measurable impact.
- Experience with user behavior modeling, marketing attribution, or real-time decision systems.
- Understanding of MLOps practices, including CI/CD for ML, model monitoring, and retraining pipelines.
- Familiarity with communication channels and platforms (e.g., push, email, in-app messaging).