Forecasting Machine Learning Research Lead
5 days ago
Winnipeg, Manitoba, Canada
Kinaxis
Full-time
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Machine-learning research and forecasting science anchor this Senior Staff Applied Scientist role with Kinaxis in Canada. Ottawa and Toronto positions are hybrid; other Canadian locations are remote.
You will lead scientific direction for a forecasting platform serving enterprise customers worldwide. The role combines hands-on research with technical guidance across engineering and data science. Focus areas include probabilistic methods, explainability, pretrained models, and production-scale evaluation.
Key Responsibilities:
Translate complex business goals into clear machine-learning problems Review designs and strengthen scientific judgment across teams Advance probabilistic, pretrained, and explainable forecasting Set benchmarks, accuracy metrics, and validation standards Partner on scalable data and machine-learning architecture Requirements:
Master's degree in computer science, statistics, or related field Ph.D. preferred with deep forecasting expertise Strong software-engineering practices and Python proficiency Experience productizing research and proofs of concept Strong written and verbal technical communication Shape Kinaxis forecasting research through applied machine learning, reproducibility, explainability, and enterprise-scale scientific standards.
Translate complex business goals into clear machine-learning problems Review designs and strengthen scientific judgment across teams Advance probabilistic, pretrained, and explainable forecasting Set benchmarks, accuracy metrics, and validation standards Partner on scalable data and machine-learning architecture Requirements:
Master's degree in computer science, statistics, or related field Ph.D. preferred with deep forecasting expertise Strong software-engineering practices and Python proficiency Experience productizing research and proofs of concept Strong written and verbal technical communication Shape Kinaxis forecasting research through applied machine learning, reproducibility, explainability, and enterprise-scale scientific standards.