Applied Machine Learning Scientist

Our client, Velan, is a global leader in the design and manufacture of industrial valves. Founded in Montreal, the company is recognized for the quality of its products, its technical expertise, and its human and collaborative corporate culture.
They are currently looking at adding an Applied Machine Learning Scientist, who will develop, deploy, and continuously improve scalable AI/ML solutions that solve meaningful business problems and improve decision-making, workflow efficiency, and process automation across the organization. Working at the intersection of data science, software engineering, and MLOps, you will transform prototypes into reliable production systems, ensure models remain accurate and trustworthy over time, and collaborate with cross-functional stakeholders to translate business needs into practical, high-quality technical solutions.
What Velan OffersIn addition to meaningful work that matches your ambitions and provides diverse learning opportunities, joining
Velan
means becoming part of a passionate team and enjoying exceptional working conditions:
Competitive salary and bonus program
Employer contribution of 5% to a retirement savings plan
Competitive vacation policy
Paid holiday shutdown during the Christmas season
Reimbursement of professional association membership fees
Annual gym membership reimbursement and other wellness initiatives
Free on-site parking
Company events (health, family, sports, etc.)
Fast-track career growth and professional development opportunities
Responsibilities
Design, develop, and deploy machine learning models that address high-value business problems and support scalable process automation.
Build and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, evaluation, deployment, inference, and monitoring.
Convert proof‑of‑concept models into production‑grade solutions that are scalable, robust, performant, and reliable across changing data conditions.
Support the development of autonomous and multi‑agent systems that can reason, plan, execute tasks, and integrate with enterprise tools, databases, APIs, and services.
Monitor deployed models for performance degradation, data drift, bias, and fairness, and retrain or recalibrate models when needed.
Partner with domain experts, engineers, and business stakeholders to define requirements, frame problems, translate objectives into technical solutions, and communicate results clearly.
Apply software engineering best practices, including version control, code review, automated testing, CI/CD, containerization, and orchestration.
Contribute to ML system architecture and technology decisions, ensuring alignment with the company’s data platforms, infrastructure, and technology standards.
Promote strong data and model governance through clear documentation, reproducibility, explainability, and auditability of ML solutions.
Qualifications
Bachelor’s degree in Computer Science, Mathematics, Software Engineering, Data Science, or a related technical field.
3 to 5 years of experience with MLOps and ML lifecycle management, including model monitoring, drift detection, model versioning, feature stores, and automated retraining.
Strong programming proficiency in Python and R, with familiarity with additional languages such as Java or C++.
Hands‑on experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, and scikit‑learn.
Solid understanding of machine learning and deep learning fundamentals, including common architectures, training approaches, and evaluation methods.
Strong software engineering fundamentals, including data structures, algorithms, complexity, system design, version control, testing, and modular code development.
Experience working with data using SQL or other query languages, including large‑scale data manipulation, preprocessing, and feature engineering.
Excellent communication and collaboration skills, with the ability to explain technical concepts to non‑technical stakeholders and work effectively across functions.
Preferred Qualifications
Experience deploying ML solutions on cloud platforms such as Azure, AWS, or GCP, using containerization and orchestration tools such as Docker and Kubernetes.
Familiarity with emerging AI/ML domains such as large language models, computer vision, reinforcement learning, graph machine learning, or agentic systems.
Demonstrated experience with model explainability, fairness assessment, and bias detection or mitigation.
Evidence of strong interest in AI/ML through open‑source contributions, publications, applied projects, competitions, or continuous learning.
If you recognize yourself in this profile and are excited about this opportunity, we would be delighted to learn more about you!
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