Senior Staff Applied Scientist, Time Series & Demand Forecasting

🔥 14 hours ago

🇨🇦 Canada – Remote

⏰ Full Time

🟠 Senior

🧬 Research Scientist

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Kinaxis

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

💰 $33M Venture Round on 2000-05

Consulting • Healthcare • Manufacturing

Kinaxis is a leading provider of supply chain orchestration solutions, specializing in AI-infused supply chain planning and management. Their flagship platform, Maestro, enables organizations to master complexity and optimize decision-making processes across the entire supply chain. By integrating advanced analytics with real-time data visibility, Kinaxis helps businesses improve operational efficiency, predict demand, and respond proactively to disruptions.

📋 Description

• Set the forecasting science agenda by defining the research roadmap and translating complex business goals into clear ML problems, measurable success criteria, and technical priorities • Provide technical direction, mentor senior scientists and ML engineers, review designs, and strengthen scientific judgment across teams • Guide evaluation and development across classical time-series, machine-learning, probabilistic, and pretrained forecasting approaches • Define accuracy metrics, benchmarks, quality gates, backtesting standards, and reproducible experiment-to-validation practices • Partner with architects, MLOps, platform, and data-infrastructure teams to ensure solutions support millions of series and daily forecasting • Explain scientific trade-offs to technical and executive audiences and build alignment across accuracy, scalability, cost, operational simplicity, and commercial priorities • Establish a fixed, reproducible accuracy benchmark within six months • Deliver benchmark-verified accuracy improvements to production within the first year

🎯 Requirements

• Deep, hands-on expertise in time-series or demand forecasting, including classical statistical methods, modern ML approaches, and deep-learning or foundation-model methods • Strong knowledge of backtesting, hierarchical and intermittent demand, covariates, forecast evaluation, probabilistic and quantile forecasting, calibration, and uncertainty • Master's degree in computer science, statistics, operations research, or a related quantitative field • 8+ years of experience developing, testing, debugging, and optimizing complex ML solutions in Python, with strong software-engineering practices • Experience moving research and proofs of concept into maintainable production systems with measurable impact • Proficiency with distributed data tooling such as Spark, cloud platforms such as GCP or Azure, containers, and distributed computing • Practical experience with model explainability, reproducibility, evaluation standards, and ML quality gates • Demonstrated ability to shape technical direction across multiple teams, influence roadmap and investment decisions, mentor others, and remain hands-on • Strong written and verbal communication, including the ability to align technical teams and executive stakeholders around a scientific strategy • Experience with pretrained time-series models such as Chronos, TabPFN-TS, or TimesFM, and with LLM-assisted configuration or analytics tooling • Supply chain or manufacturing knowledge, including demand planning, S&OP, replenishment, or optimization, and experience forecasting hundreds of thousands to millions of series • Experience productizing data-science capabilities for non-experts and building automated configuration, calibration, or accuracy assistants

🏖️ Benefits

• Flexible vacation and Kinaxis Days (company-wide days off) • Flexible work options • Physical and mental well-being programs • Regularly scheduled virtual fitness classes • Mentorship programs, training, and career development • Recognition programs and referral rewards • Hackathons • Accessibility accommodations upon request