Senior Staff Applied Scientist, Time Series & Demand Forecasting
##高级工作人员应用科学家，时间系列和需求预测位置 CA-远程 ID 2026-35574 类别工程职位类型 全日制远程是的关于基纳克西斯 你是否想加入一个创新性的市场领先公司，在那里你真正可以提高你的职业生涯？在基纳克希斯我们认真对文化，我们认真对于技术，我们认真的对客户，我们认 serious 不过认真。如果你想要成为一个令人难以置信的增长故事的一部分，那么我们可能是适合你的地方！1984 年，我们作为三名工程师团队开始了。今天，我们已经发展成为一个全球组织，拥有全球2000多名员工，6个全球办事处和加拿大奥塔瓦的顶级总部。作为全球几项最佳雇主奖获得者，我们自豪地与客户和员工合作解决供应链面临的一些最大挑战。基纳克西斯是现代供应链编排领域的全球领导者，推动复杂的全球供应链，并支持管理人员。我们的强大的 AI 注入平台在端到端供应链中提供了全面透明度和可见性，使客户能够更快速，更好地做出决策。我们的团队在100多个国家拥有超过4万用户，我们正在扩大我们的团队，因为我们继续创新和革命地支持客户。位置奥塔瓦和多伦多，加拿大 - 混合其他加拿大地点 - 远程关于团队。我们的人工智能团队为包括消费包装商品，零售和生命科学等行业开发机器学习解决方案，实现预测，细分化，解释性和优化。我们将严格的科学与实际商业问题结合在一起，我们的工作被部署到全球企业客户。需求预测是 Kinaxis 平台的核心。我们正在建立一个专门的数据科学和人工智能研究引擎，以提供企业规模准确，自行校准，可解释和经济效率的预测。**关于角色** 我们寻求一名具有时间系列和需求预测方面的深入专业知识的高级工作人员应用科学家来为我们的预测平台设定科学的方向。这是没有直接报告的实践上高级个人贡献者的职位。你将定义科学，建立技术能力。
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## Senior Staff Applied Scientist, Time Series & Demand Forecasting
Location
CA-Remote
ID
2026-35574
Category
Engineering
Position Type
Full-Time
Remote
Yes
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 serious 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.
Kinaxis is a global leader in modern supply chain orchestration, powering complex global supply chains, and supporting the people who manage them. Our powerful, AI infused platform provides full transparency and visibility across end-to-end supply chains, enabling our customers to make faster, better decisions. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption. 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.
Location
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, 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; 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 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.
- Nice to Have
- Publications, patents, open-source contributions, industry benchmarks, or production systems with demonstrated impact.
- 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.
**#Senior; #LI-EM1**
**Work With Impact:** 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 are available for our loved ones, and so much more.
**Work with Fortune 500 Brands:** Companies across industries trust us to help them take control of their integrated business planning and digital supply chain. Some of our customers include Lockheed Martin, Unilever, P&G, ExxonMobil, Cisco and more.
**Social Responsibility at Kinaxis:** Our Diversity, Equity, and Inclusion Committee weighs in on hiring practices, talent assessment training materials, and mandatory training on unconscious bias and inclusion fundamentals. Sustainability is key to what we do and we’re committed to a long-term net-zero operations strategy. We are involved in our communities and support causes where we can make the most impact.
_People matter_ at Kinaxis and here are some of the perks and benefits we offer, which may vary by location and employee:
- 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
For more information, visit the Kinaxis website at [www.kinaxis.com](https://www.kinaxis.com/en) or the company’s blog at [http://blog.kinaxis.com](https://www.kinaxis.com/en/blog).
Kinaxis welcomes candidates to apply to our inclusive community. We provide accommodations upon request to ensure fairness and accessibility throughout our recruitment process for all candidates, including those with specific needs or disabilities. If you require an accommodation, please reach out to us at recruitmentprograms@kinaxis.com. This contact information is for accessibility requests only and cannot be used to inquire about the status of applications.
_Kinaxis is committed to ensuring a fair and transparent recruitment process. We use artificial intelligence (AI) tools in the initial step of the recruitment process to compare submitted resumes against the job description to identify candidates whose education, experience, and skills most closely match the requirements of the role. After the initial screening, all subsequent decisions regarding your application, including final selection, are made by our human recruitment team. AI does not make any final hiring decisions._
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