As part of grocery replenishment organization, Sr. Applied Scientists own inventory optimization, distribution optimization, and end-to-end modeling / simulation of Amazon grocery supply chain utilizing optimization and machine learning toolsets. We are looking for a talented and experienced applied scientist with a passion for designing and implementing elegant scientific solutions for Amazon-scale problems.

• Key job responsibilities
• Design and develop advanced mathematical optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of inventory optimization, distribution optimization, network design, and control theory.
• Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.
• Research, prototype, simulate, and experiment with these models using modeling languages such as Java, Python, MATLAB, Mosel or R; participate in the production level deployment.
• Closely work with software engineering teams and write production well-tested Java code for science modules within engineering-managed services. Provide time-sensitive on-call support and high-severity issue support when bugs are identified in production code. Improve code quality of legacy scientific production code.
• Create, enhance, and maintain technical documentation and science designs.
• Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders.
• Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.
• Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists.
• A day in the life
• Engage with customers to understand their problems.
• Collaborate with product partners and peers to design and deliver algorithmic solutions to these problems.
• Implement these solutions in java within engineering systems through close collaboration with engineering partners achieving high code quality.
• Deploy and measure impact of implementations.
• Support customers and stakeholders whenever deep-dives and enhancements are needed as they relate to scientific products the team owns.
• Contribute to product roadmap through new innovations on behalf of customers.
• Publish work in internal and external scientific community.
  About the team
  SCOT IB GRO Science team is comprised of applied scientists with strong optimization & ML science depth and object-oriented programming & design patterns knowledge. Given the scale of problems we solve for our customers and mission-critical nature of our solutions, systems thinking driven approach, with attention to algorithmic complexity, solution quality, simplicity, and extensibility are of critical importance. We collaborate with engineering teams closely and prioritize solving problems with minimally complex solutions while maintaining quality. We build solutions that must consistently improve customer experience with maximum transparency and explainability of decisions made by such solutions. We strive for every member of the team to be knowledgeable about every product that the team owns to enable meaningful collaboration within the team. We seek to publish our work at internal and external scientific communities when they produce novel solutions.

Basic Qualifications

• PhD in operations research, applied mathematics, theoretical computer science, or equivalent
• 3+ years of building machine learning models or developing algorithms for business application experience
• 3+ years of industry or academic research experience
• Knowledge of programming languages such as C/C++, Python, Java or Perl

Preferred Qualifications

• Domain expertise in inventory and/or distribution optimization problems.
• Expertise in optimization: linear, non-linear, mixed-integer, large-scale, network, robust, stochastic, decomposition methods.
• Expertise in building optimization models and implementing them using OR tools (e.g. XPRESS, Gurobi, CPLEX, etc).
• Expertise in design and analysis of algorithms.
• Experience with object oriented programming concepts and programming in Java / Kotlin.
  Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
  Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.

CAN, ON, Toronto - 195,900.00 - 327,200.00 CAD annually