Senior Machine Learning / Data Science Engineer (offline)

Responsibilities
Revinate is seeking an enthusiastic data scientist to join our fast-growing company. You will be a team of one, to start, and will help build and enhance machine learning models and data pipelines that power Revinate’s Guest Data Platform. In this role, you will possess significant autonomy to develop our entire Machine Learning pipeline & models. A passion for building greenfield software is a must. Additionally, the ability to collaborate with others, regardless of title, to tackle complex hospitality data problems is a requirement. If you enjoy coming up with thoughtful solutions to challenging problems then we may be a match.

YOU WILL

Design and develop models and algorithms to power our next-generation platform
Add new metrics and aggregations to our existing machine learning pipeline
Drive hospitality insights and use data science to help hoteliers make smarter decisions

Skills Required
3+ years of professional experience in a Data Science role
2+ years of experience working with, and deploying to, a fully-managed machine learning service (e.g., AWS Sagemaker or GCP CloudML)
M.S. or Ph.D. in machine learning, statistics, applied math, engineering, physics, or a similar field
Experience with distributed processing frameworks (e.g., Hadoop, Spark, HBase, Hive, Mahout)
Ability to write code in at least one of the following languages: Scala, R, Python, or Java
Passion for working with large datasets and mining data to come up with insights that can power future product growth
Strong opinions about writing beautiful, maintainable, and understandable code
Proficiency in writing reproducible and fault-tolerant models and creating ML pipelines (Sampling, Feature Engineering, Training, Evaluation, and Scoring)
Expertise in statistical methods and experimental design and analysis
Experience building models to predict the growth trajectory of different customer segments
Deep understanding of Natural Language Processing frameworks and techniques to deal with highly unstructured data
Designing and analyzing experiments to measure the impact of new product features
Familiarity with querying TinkerPop-enabled databases using Gremlin
Experience building recommendation models based on graph relationships
Strong verbal and written communication skills

Project Description
We believe that hoteliers deserve better. The global hotel sector is a booming $500B+ industry. Yet, hotels are facing many complex challenges, including increased pressure from online travel agencies and intense competition from ever-growing room inventory and the shared economy. That, coupled with aging, cumbersome technology is making the job of the hotelier more challenging than ever. At Revinate, we use cutting edge technology to build powerful software for hotels to take back control and drive direct revenue. The simplicity and beautiful UX of our solutions are a breath of fresh air in an industry of old technology.

About Zoolatech

Zoolatech is a boutique service provider, specializing in high-end software development. We are based in Silicon Valley with a Development Center in Kyiv, Ukraine. Although ZoolaTech is a relatively young player on the Eastern European market, we have deep roots and years of experience of working within Ukrainian and American high tech industries. The size of our clientele varies from Fortune 500 to inspiring startups. We are not an outsourcer in a traditional sense, rather we specialize in helping our Clients scale by extending their teams to Eastern Europe.

Over the years, we have learned that in our industry people are the most important asset, and so we treat our employees as such. We set our working environment in a way that allows individuals thrive and grow professionally, as well as personally. As much as we are dedicated to providing the best possible services to our Clients, we are just as dedicated to helping our Employees reach their professional dreams and ambitions.

Company website:
https://zoolatech.com/

The job ad is no longer active
Job unpublished on 31 July 2020

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