Senior Data Scientist, UTR Optimization

Job title:

Senior Data Scientist, UTR Optimization

Company

Amazon

Job description

DESCRIPTIONLooking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team.
The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network.Key job responsibilities
The Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality.
On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy.About the team
NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions.We are open to hiring candidates to work out of one of the following locations:Toronto, ON, CANBASIC QUALIFICATIONSMasters or PhD with limited experience, OR a Bachelors degree in Statistics, Applied Math, Operations Research, Economics, Engineering or a related quantitative field with three years of working experience as a Data Scientist
Experience with statistical analysis, data modeling, optimizations, regression modeling and forecasting, time series analysis, data mining, financial analysis, and demand modeling
Experience applying various machine learning techniques, and understanding the key parameters that affect their performance
Experience in Statistical Software such as R, Weka, SAS, SPSS
Proficiency with TABLEAU or other web based interfaces to create graphic-rich customizable plots, charts data maps etc.
Able to write SQL scripts for analysis and reporting (Redshift, SQL, MySQL)
Experience using one or more programming languages (Python, R, Java, C++, MATLAB)
Experience processing, filtering, and presenting large quantities (100K to Millions of rows) of dataPREFERRED QUALIFICATIONSPrevious experience in ML, data scientist or optimization engineer role with a large technology company
Familiarity with the processes used in Amazon fulfillment/sortation network
Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
Experience in creating data driven visualizations to describe an end-to-end system
Excellent written and verbal communication skills. The role requires effective communication with colleagues from computer science, operations research and business backgrounds.
Ability to work on a diverse team or with a diverse range of coworkersAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status. If you would like to request an accommodation, please notify your Recruiter.

Expected salary

Location

Toronto, ON

Job date

Thu, 20 Jun 2024 02:41:20 GMT

To help us track our recruitment effort, please indicate in your email/cover letter where (hiring-jobs.com) you saw this job posting.

Share

Medical Laboratory Assistant, Multi Functional Lab Core Lab – Provincial Laboratory Medicine Services (PLMS)

Job title: Medical Laboratory Assistant, Multi Functional Lab Core Lab - Provincial Laboratory Medicine Services…

12 mins ago

PUBLIC STORAGE HIRING EVENT – WEDNESDAY, SEPT. 18TH, 2024

Job title: PUBLIC STORAGE HIRING EVENT - WEDNESDAY, SEPT. 18TH, 2024 Company Public Storage Job…

16 mins ago

Senior Social Worker

Job title: Senior Social Worker Company Royal Borough of Kingston Upon Thames Job description About…

18 mins ago

Customer Service Representative Customer Service Representative

hiring-jobs.com Banking Customer Service Representative At Foundever™, experience is everything: At Foundever, we deliver leading…

34 mins ago

Retail Customer Service Representative in Red Deer, Alberta

hiring-jobs.com Is this job for you? Do you believe in putting the customer first? Do…

34 mins ago

Customer Service Representative in Richmond, Virginia

hiring-jobs.com Location: 3133 E Parham Rd, Henrico, Virginia 23228 United States of America Are you…

34 mins ago
For Apply Button. Please use Non-Amp Version

This website uses cookies.