Position: Middle/Senior Python Engineer (data science)
Company: Intellias
Experience required: 3y
English level: upper

Project details: 

The team is building the library, which learns user's places and routes and provides destination prediction, traffic information and smart assistance functionality.

This is a service that will be mainly used to predict user destinations and routes for commute but also other use cases.
User trip data will be ingested, processed and saved into the user profile. The profile will later be used to predict user's destinations in the future. 

 Responsibilities:

Work closely with product owners and principal engineers to implement, test and continually improve scalable applications, services and pipelines running on Kubernetes;
Willingness to own/lead some of the components of the system
Take accountability for implementation and quality of software products as well as time and complexity estimation of own technical tasks;
Develop software products using Agile methods, tools, and continuous delivery process;
Participate in design reviews, code reviews, and product demos;
Collaborate with other engineering teams to resolve dependencies and deliver high-quality software on time.
Failure investigation
Regular communication with the remote team
 
Technology Stack:

Scala/Java using frameworks: Play, Quarkus
Python3
Data processing using Spark, Flink
DevOps: Kubernetes, Helm, GitLab, Docker

Requirements:
Strong knowledge and hands-on experience with Python3
Experience working with Pandas, sci-kit learn, Jupiter notebooks
Background in following:
statistics/statistical analysis
patterns learning
Bayesian statistics and predictors
probability theory (good undestanding)
machine learning (no advanced ML needed)
Good communication skills in English, both written and spoken,  ability to articulate and communicate clearly complex topics
Self-motivated, well-organized, detail-oriented and a very strong team player
Understanding of Agile methodologies and engineering practices
 
Nice to have:

Experience with building big data tools and toolchains (Spark, Hadoop), data processing
Bayesian inference knowladge