Lead Data Scientist, UTR Enhancement

3 weeks ago

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

Seeking a New Opportunity? Amazon Canada Fulfillment Services, ULC is on the lookout for a talented and driven Data Scientist to become a part of the NASC Engineering Data, Product, and Simulation Team.

The Sort Center network serves as a vital Middle-Mile solution within the Amazon Transportation Services (ATS) division, connecting Fulfillment Centers to the Last Mile.

The customer experience hinges on our capability to effectively manage volume flow through the middle-mile network.


Key Responsibilities

The Data Scientist will be responsible for designing and executing solutions to tackle intricate business challenges through simulation. In this position, you will utilize advanced analytical techniques and statistical methodologies to extract insights from extensive datasets, while also developing intuitive simulations and data visualizations.

Your contributions will span across various layers of a data solution, collaborating closely with process design engineers, business intelligence engineers, and technical product managers to gather relevant datasets, construct simulation models, and present key findings to business leaders and stakeholders.

Your work will strike a balance between scientific rigor and business applicability.

As a member of this team, you will significantly influence the entire NASC organization, with ample opportunities for learning and professional growth within the NASC Engineering team.

This position will be the inaugural dedicated simulation expert, providing you with a unique chance to establish and promote best practices for simulation within our team.

To excel in this role, you must be adept at transforming ambiguous business inquiries into clearly defined problems, developing measurable metrics, and delivering results that adhere to high standards of data quality, security, and privacy.


About the Team

The Product and Analytics Team within NASC Engineering is dedicated to creating tools for simulation and optimization, catering to the needs of both internal and external stakeholders, including Process Design Engineering, NASC Engineering, ACES, Finance, Safety, and Operations.

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

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

We collaborate with diverse functional teams to test and pilot new hardware and software solutions.


Basic Qualifications
  • Masters 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 professional experience as a Data Scientist.
  • Experience in statistical analysis, data modeling, optimization, regression modeling, forecasting, time series analysis, data mining, financial analysis, and demand modeling.
  • Proficiency in applying various machine learning techniques and understanding the key parameters that influence their performance.
  • Familiarity with statistical software such as R, Weka, SAS, SPSS.
  • Proficient in TABLEAU or similar web-based interfaces for creating graphic-rich, customizable plots, charts, and data maps.
  • Ability to write SQL scripts for analysis and reporting (Redshift, SQL, MySQL).
  • Experience with one or more programming languages (Python, R, Java, C++, MATLAB).
  • Skilled in processing, filtering, and presenting large datasets (ranging from 100K to millions of rows).

Preferred Qualifications
  • Prior experience in a machine learning, data scientist, or optimization engineer role within a large technology organization.
  • Familiarity with processes utilized in Amazon's fulfillment and sortation network.
  • Capability to develop experimental and analytical plans for data modeling processes, employing robust baselines, and accurately determining cause-and-effect relationships.
  • Experience in creating data-driven visualizations to illustrate an end-to-end system.
  • Excellent written and verbal communication skills, with the ability to effectively engage with colleagues from computer science, operations research, and business backgrounds.
  • Able to work collaboratively within a diverse team or with a varied range of coworkers.

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