Position: Data Scientist
Company: Grid Dynamics
Experience required: 3y
English level: upper

It is a functionality to predict and display the actual delivery date when a shipment is expected to arrive in the customer’s hand when an order is checked-out on the client's website. 
This algorithm will be derived from 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 delivery date 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 a phased manner for all product categories.

**Details on the tech stack:**
Google Cloud Platform + MongoDB/SQL + Python

**Responsibilities:**
- 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/modeling for other people within the team

**Requirements:**
- 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 in developing cloud-based applications (ideally using Google Cloud Platform and Kubeflow) is a big plus