Scientist Machine LearningScientifique en apprentissage automatique

IPEX Group•Oakville, MB

4d•CA$91,300 - CA$110,000•Hybrid

About The Position

The Applied Machine Learning Scientist develops, validates, and operationalizes machine-learning and statistical solutions that improve supply-chain decisions. The role is primarily focused on demand forecasting, inventory optimization, and related decision-support problems. The successful candidate will combine strong applied modelling skills with the ability to work independently in Python and SQL, build reliable model-ready datasets, and translate experiments into maintainable production solutions. The role works closely with the Manager of Advanced Analytics & Machine Learning, and with business and technical stakeholders to ensure solutions are measurable, explainable, scalable, and aligned with operational constraints.

Requirements

Master's degree in Data Science, Statistics, Computer Science, Mathematics, Operations Research, or a related quantitative field; or a bachelor's degree with equivalent applied machine-learning experience.
Three or more years of applied data-science or machine-learning experience, including meaningful ownership of a production modelling solution.
Demonstrated experience taking a modelling problem from raw data through feature engineering, validation, implementation, and performance review.
Strong Python skills, including practical use of pandas, NumPy, scikit-learn, and gradient-boosting libraries such as LightGBM or XGBoost.
Strong SQL skills and the ability to independently create, validate, and troubleshoot large analytical datasets.
Strong foundation in statistics, experimental design, time-series validation, model evaluation, and leakage prevention.
Ability to design solutions that respect real operational constraints and balance model performance with maintainability and adoption.
Clear written and verbal communication with both technical and business audiences.

Nice To Haves

Experience with demand forecasting, inventory, supply-chain analytics, manufacturing, or operations research.
Experience with Snowflake or another modern cloud data platform.
Familiarity with Git, automated testing, model versioning, monitoring, and reproducible ML workflows.
Exposure to MLOps, deep learning, or generative AI where relevant to an applied business problem.
Credentials or practical knowledge in operations, supply chain, Lean, or CPIM are an asset.

Responsibilities

Develop and improve forecasting and optimization models for supply-chain and operational use cases.
Perform feature engineering, model selection, hyperparameter tuning, error analysis, and structured experimentation.
Design evaluation frameworks using time-aware validation, backtesting, and business-relevant performance metrics.
Apply appropriate statistical and machine-learning methods, including time-series analysis, regression, probabilistic modelling, and tree-based models.
Investigate model bias, uncertainty, drift, and failure modes, and recommend practical improvements.
Write clear, maintainable, production-quality Python and SQL.
Build and maintain scalable model workflows and model-ready datasets using Snowflake and collaborative analytics environments such as HEX.
Operationalize model outputs through reliable data pipelines, versioning, monitoring, retraining, and alerting practices.
Contribute to shared coding, testing, documentation, and model-governance standards.
Own defined modelling workstreams from problem formulation through validation, implementation, and business adoption.
Partner with Supply Chain, IT, Data, and other stakeholders to define objectives, constraints, and success measures.
Present technical findings clearly and translate model results into actionable business recommendations.
Evaluate emerging methods and technologies based on their practical value to current business problems.