Are you looking to join an innovative, market-leading company where you can truly elevate your career? At Kinaxis we are serious about culture, we are serious about technology, we are serious about customers, and we are serious about not taking ourselves too seriously. Today, we have grown to become a global organization with over 2000 employees around the world, 6 global office and a best-in-class HQ in Ottawa, Canada. As winners of several Top Employer awards globally, we are proud to work with our customers and employees towards solving some of the biggest challenges facing supply chains today. Our powerful, AI infused platform provides full transparency and visibility across end-to-end supply chains, enabling our customers to make faster, better decisions. With more than 40,000 users in over 100 countries, we are expanding our team as we continue to innovate and revolutionize how we support our customers. Other Canadian locations
- Remote Our AI team develops machine-learning solutions across forecasting, segmentation, explainability, and optimization for industries including Consumer Packaged Goods, Retail, and Life Sciences. We combine rigorous science with real business problems, and our work is deployed to enterprise customers worldwide. We are building a dedicated data-science and AI research engine to deliver forecasting that is accurate, self-calibrating, explainable, and cost-effective at enterprise scale. We are seeking a Senior Staff Applied Scientist with deep expertise in time-series and demand forecasting to set the scientific direction for our forecasting platform. This is a hands‑on senior individual‑contributor role with no direct reports. You will define the science, establish technical standards, and guide multiple engineering and data‑science teams to deliver the roadmap.



You will own the scientific strategy and measurement framework used to improve forecasting accuracy, robustness, and credibility. You will also influence long‑term technical direction as the platform scales to millions of series and daily forecasting. Set the forecasting science agenda. Define the research roadmap and translate 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. Establish scientific rigor. Define accuracy metrics, benchmarks, quality gates, backtesting standards, and reproducible experiment‑to‑validation practices. Partner with architects, MLOps, and platform and data‑infrastructure teams to ensure solutions can 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. Within six months, a fixed and reproducible accuracy benchmark is in place, teams report against it, and the research roadmap is aligned with engineering and product leadership. Within the first year, benchmark‑verified accuracy improvements have shipped to production, and teams independently design and evaluate experiments using the standards you established. Deep,



hands‑on expertise in time‑series or demand forecasting, including classical statistical methods, modern ML approaches, and deep‑learning or foundation‑model methods. 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 solid 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. Our platform directly helps companies power the world’s supply chains. We see the results of what we do out in the world every day, when we see store shelves stocked, when medications a

📌 Senior Staff Applied Scientist, Time Series (Quebec City)
🏢 Kinaxis
📍 Quebec City