Senior mixed - methods researcher leading quantitative and qualitative studies for Pulse Labs, which delivers human - centered product insights to technology companies. Translating multimodal data into strategic recommendations.
About the role
- Senior Staff Applied Scientist defining time-series forecasting science for Kinaxis, a global supply-chain orchestration company. Guiding ML research, evaluation, and production scale-up.
Responsibilities
- 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
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