About Kinaxis

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 responsible about technology, we are serious about customers, and we are serious about not taking ourselves too seriously. If you are looking to be part of an incredible growth story, then we might just be the place for you

In 1984, we started out as a team of three engineers. 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.

Ottawa and Toronto, Canada - Hybrid. Other Canadian locations - Remote.

About The Team

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.

Demand forecasting is central to the Kinaxis platform. 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.

About The Role

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.

Vacancy Status

This is an existing job vacancy

What you will do

- Set the forecasting science agenda. Define the research roadmap and translate complex business goals into clear ML problems, measurable success criteria, and technical priorities.
- Lead through influence. Provide technical direction, mentor senior scientists and ML engineers, review designs, and strengthen scientific judgment across teams.
- Advance forecasting capabilities. Guide evaluation and development across classical time-series, machine-learning, probabilistic, and pretrained forecasting approaches, including hierarchical and intermittent demand, covariates, cold starts, segmentation, explainability, and domain adaptation.
- Establish scientific rigor. Define accuracy metrics, benchmarks, quality gates, backtesting standards, and reproducible experiment-to-validation practices.
- Shape architecture for scale. Partner with architects, MLOps, and platform and data-infrastructure teams to ensure solutions can support millions of series and daily forecasting.
- Communicate and align.



Explain scientific trade-offs to technical and executive audiences and build alignment across accuracy, scalability, cost, operational simplicity, and commercial priorities.

What Success Looks Like

- 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.

What we are looking for

- Deep, hands-on expertise in time-series or demand forecasting, including classical statistical methods, contemporary 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; Ph.D. preferred.
- 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 roa

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