Position: Data Scientist
Company: Grid Dynamics
Experience required: 5y
Category: Data Science
English level: intermediate
Published: 2022-07-01T00:00:00+03:00

Macy’s along with Bloomingdales is one of the oldest and biggest department store chains in USA. As of 2015, Macy's was the largest U.S. department store company by retail sales. As of January 31, 2021, there were 512 stores. Macy's catalog contains almost 1 million products. The company had 130,000 employees and earned annual revenue of $24.8 billion.
Web site for a large American department store chain - Macy’s and Bloomingdale’s.
More than 50 applications for work in stores have been developed in Macy's.
These engineers will be deployed for Supply Chain group at Macy’s to build a new feature called, “Precise Delivery Date”. PDD is a functionality to predict and display actual delivery date when a shipment is expected to arrive in customer’s hand when an order is checked-out on Macys.com website. This algorithm will be derived by multiple tasks and logistic actions from the time an order is check-out - Processed - Shipped . Delivered. For every item in a check-out bag, the PDD will be displayed for the customer. This functionality will be rolled out first as MVP and then after training and analytics, will be deployed in phased manner for all product categories.

 Details on tech stack:

Google Cloud Platform + mongodb/sql + python

- Developing and optimizing machine learning models, primarily for recommender systems
- Designing and implementing training pipelines and serving applications using Google Cloud Platform / Kubeflow
- Evaluating recommender systems
- Reviewing the code and helping with code design / modelling to other people within the teamMain requirements to candidates should be:
- Very good understanding of Python, experience working with large scale projects
- At least some knowledge of SQL
- General understanding of machine learning, practical experience with ML algorithms (boosting libraries, neural networks etc)
- Experience in developing recommender systems is a huge plus
- Experience is developing cloud based applications (ideally using Google Cloud Platform and Kubeflow) is a big plus