Staff Applied Scientist (Distribution Center Solutions)

Accountabilities: Lead research and development efforts for AI/ML-driven replenishment and optimization systems within large-scale distribution center operations.

Design and implement advanced models for demand forecasting, inventory decay, price elasticity, promotions, and stochastic supply chain behavior.

Develop and optimize multi-echelon inventory control systems using techniques from machine learning, operations research, and stochastic optimization.

Translate complex mathematical and research concepts into production-grade systems using scalable and well-tested code.

Own the end-to-end lifecycle of modeling initiatives, from research and experimentation to deployment and performance monitoring.

Evaluate model performance rigorously through simulation, A/B testing, and real-world validation frameworks.

Collaborate with engineering and product teams to define technical direction and align research priorities with business impact.

Mentor other scientists and engineers, raising the bar for experimental rigor, modeling quality, and system design.

Contribute to architectural decisions and ensure scalability, reliability, and maintainability of production systems.

Continuously explore and integrate new methodologies in AI, optimization, and decision science to improve system performance.

Requirements

Advanced degree (MS or PhD) in Operations Research, Industrial Engineering, Computer Science, Electrical Engineering, Mathematics, or a related quantitative field.

4+ years of industry experience for PhD holders or 8+ years for MS holders working on applied machine learning, optimization, or decision systems.

Strong background in stochastic optimization, forecasting, simulation, or large-scale decision-making under uncertainty.

Proven experience building and deploying production systems that integrate ML models with real-world operational constraints.

Strong programming skills in Python and related data/ML stacks such as NumPy, PyTorch, and pandas.

Experience modeling complex systems such as supply chains, inventory optimization, or dynamic pricing is highly desirable.

Ability to clearly communicate complex technical and mathematical concepts to both technical and non-technical stakeholders.

Strong experimental mindset with experience in designing, validating, and iterating on ML-driven systems.

Excellent collaboration skills and experience working cross-functionally with engineering and product teams.

Nice to have: familiarity with distributed systems, ML platforms, or applied research in operations research or reinforcement learning.

Benefits

Fully remote work within Canada

Competitive salary range aligned with senior applied science roles

Comprehensive health, dental, and vision coverage for employees and families

Mental health and wellness support programs

Generous paid time off and flexible working arrangements

Home office and coworking stipends for flexible work setup

Annual professional development budget for continuous learning

Equity package (where applicable) and long-term incentive opportunities

High-impact role with measurable contribution to reducing global food waste

Collaborative, research-driven environment with strong focus on innovation and experimentation.

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