Position: Senior Data Scientist / ML Engineer |
Company: Xenoss
Category: Data Science
Experience required: 5y
English level: intermediate

Required skills

— Degree in applied math, computer science, or equivalent.
— 5+ year experience with a SQL-like query language, Python, and Spark
— Good communicator who can explain and understand complex problems while dealing with both non-
technical and technical teams.
— Ability to query large amounts of data, build models, and derive insights for customers.
— Ability to structure and solve difficult problems with minimal supervision.
— A focus on details and willingness to learn.
— Good verbal and written English communication skills (Intermediate level or higher).

Do not hesitate to apply if you are missing some specific experience. We will be happy to help you with gaining one

As a plus

— Experience in online mobile advertising is a plus.
— Experience with Zeppelin and AWS Redshift is an advantage.

We offer

— Teams of people who love programming
— Complex technical challenges with big data/AI/high load
— Freedom to make your own engineering decisions and broad space for creativity
— Modern technology stack to work with
— Work remotely or from the office options on a flexible schedule
— Long-lasting projects
— Financial compensation for professional events and education
— Opportunity to choose the equipment you like
— Above-market compensation

Responsibilities

The role is very hands-on, and If you are keen to join an early-stage startup within the ad-tech industry and be part of a fantastic team then this is the opportunity for you.

ML model building using cutting-edge algorithms (Deep Learning, XGBoost, GBM, etc.) from a proof of concept to productionised models, monitoring the performance of live models bidding in real-time, and producing insight for customers (understand what drives installs/retention behaviours).
Continuous research and improvements on productionised models, including ML feature engineering and model training tuning to reflect changes in users’ behaviours and market dynamics.
Collaborating closely with the commercial team to identify, develop, and optimise business opportunities.
Generating insight from campaign data using Python, Tableau, and other technologies to satisfy customers’ needs.
Reviewing, monitoring, and optimising running campaigns and integrations providing advice and troubleshooting for customers.

Project description

ML model building using cutting-edge algorithms (Deep Learning, XGBoost, GBM, etc.) from a proof of concept to productionised models, monitoring the performance of live models bidding in real-time, and producing insight for customers (understand what drives installs/retention behaviours).
Continuous research and improvements on productionised models, including ML feature engineering and model training tuning to reflect changes in users’ behaviours and market dynamics.
Collaborating closely with the commercial team to identify, develop, and optimise business opportunities.
Generating insight from campaign data using Python, Tableau, and other technologies to satisfy customers’ needs.
Reviewing, monitoring, and optimising running campaigns and integrations providing advice and troubleshooting for customers.