Lead Data Scientist for UTR Optimization

3 weeks ago

Toronto, Ontario, Canada Amazon Canada Fulfillment Services, ULC Full time

Are you ready to take on a pivotal role? Amazon Canada Fulfillment Services, ULC is seeking a talented and driven Data Scientist to enhance our North America Sort Centers (NASC) Engineering Data, Product, and Simulation Team.

The Sort Center network serves as a vital Middle-Mile solution within the Amazon Transportation Services (ATS) group, connecting Fulfillment Centers to the Last Mile. The efficiency of our operations directly influences customer satisfaction.

Key Responsibilities:

  • Design and implement innovative solutions to tackle intricate business challenges through simulation.
  • Utilize advanced analytical techniques and statistical methodologies to extract insights from extensive datasets, while creating user-friendly simulations and data visualizations.
  • Collaborate closely with process design engineers, business intelligence engineers, and technical product managers to gather relevant datasets and develop simulation models, while reviewing key outcomes with business leaders and stakeholders.

Your contributions will balance scientific rigor with practical business applications.

Impact and Growth:

As a member of this team, you will significantly influence the entire NASC organization, with ample opportunities for learning and professional development within the NASC Engineering team. This position represents the first dedicated simulation expert role, providing a unique chance to establish and promote best practices in simulation across our team.

Success Factors:

To excel in this position, you must effectively translate ambiguous business inquiries into well-defined problems, develop measurable metrics, and deliver results that adhere to high standards of data quality, security, and privacy.

About the Team:

The NASC Engineering's Product and Analytics Team is dedicated to creating tools for in-house simulation and optimization, catering to the needs of our internal and external stakeholders, including Process Design Engineering, NASC Engineering, ACES, Finance, Safety, and Operations.

We engineer data science tools to assess various design and operational scenarios for both new and existing sort centers, ensuring their robustness, stability, scalability, and cost-effectiveness.

Our team conceptualizes innovative data science solutions, leveraging optimization and machine learning platforms to analyze processes, eliminate non-value-added steps, and enhance overall performance.

Basic Qualifications:

  • Master's or PhD with limited experience, or a Bachelor's degree in Statistics, Applied Mathematics, Operations Research, Economics, Engineering, or a related quantitative field with three years of experience as a Data Scientist.
  • Proficiency in statistical analysis, data modeling, optimization, regression modeling, forecasting, time series analysis, data mining, financial analysis, and demand modeling.
  • Experience with various machine learning techniques and understanding the key parameters influencing their performance.
  • Familiarity with statistical software such as R, Weka, SAS, SPSS.
  • Ability to create graphic-rich, customizable visualizations using TABLEAU or similar web-based interfaces.
  • Competence in writing SQL scripts for analysis and reporting (Redshift, SQL, MySQL).
  • Experience with programming languages (Python, R, Java, C++, MATLAB).
  • Ability to process, filter, and present large datasets (ranging from 100K to millions of rows).

Preferred Qualifications:

  • Previous experience in a machine learning, data scientist, or optimization engineer role within a large technology organization.
  • Familiarity with processes in the Amazon fulfillment/sortation network.
  • Capability to develop experimental and analytical plans for data modeling processes, utilizing strong baselines to accurately determine cause-and-effect relationships.
  • Experience in creating data-driven visualizations to illustrate end-to-end systems.
  • Exceptional written and verbal communication skills, with the ability to effectively engage with colleagues from diverse backgrounds in computer science, operations research, and business.
  • Aptitude for working within a diverse team or with a wide range of coworkers.

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