Sr. Applied Scientist, SCOT-Inbound Systems
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Job Location:
Monthly Salary:
Posted on:
15 hours ago
Vacancies:
1 Vacancy
Job Summary
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 organizations long-term roadmap and resourcing onboard new technologies onto Science teams 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.
- 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
- 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 for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
CAN ON Toronto - 195900.00 - 327200.00 CAD annually
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 organizations long-term roadmap and resourcing onboard new technologies onto Science teams 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.
- 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
- 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 for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
The base salary range for this position is listed below. As a total compensation company Amazons 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 - 195900.00 - 327200.00 CAD annually
Required Experience:
Senior IC
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
About Company
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