Senior Machine Learning Engineer

  • Overview

    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.

  • 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.


    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

  • We Require

    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 (Spark is a must)

    Ability to write code in Scala

    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

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