Product Director for Hotels Consolidator

RateHawk
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Machine Learning Engineer

Senior Director, Data Science and Analytics

Product Data Scientist

Master Data Analyst

Data Engineering Manager

  • Increase bookings and revenue by enhancing search speed, improving ranking quality, refining the accuracy of content, and boosting the success rate of bookings;
  • Manage product teams, where you will be assisted by product managers;
  • Collaborate closely with analysts to thoroughly understand business logic and data, then implement the insights gained;
  • Together with development and architects, look for a balance between quality and speed of solution. Also, don't be afraid of legacy :)
  • Help with the execution of key projects for the business and dig deep when it makes sense;
  • Your focus for this position will be distributed roughly like this: 40% people management, 40% key project, 20% strategy formation.

Requirements

  • Have 4+ years of experience in product management, including 2+ years leading teams to develop complex technology products. Such as: search, recommendations, high-load APIs, analytics platforms, or cloud products;
  • As a former developer/data scientist/product analyst, you leverage your technical background to make informed management decisions that align with business needs, while keeping technology considerations at the forefront;
  • Attentive to details and love to deep drive into data but know where you need to make decisions based on vision;
  • You know how to develop people through coaching.

Benefits

  • A fully flexible work schedule — there’s no pressure to start work at exactly 9:00 AM; what matters is achieving results and moving forward;
  • Each person in our team is encouraged to choose their preferred work format. You can work fully remotely, come to the office, or choose a hybrid work model;
  • We are an ambitious and supportive team who love what they do, appreciate each other, and grow together;
  • The growth and development of each employee is our priority, so we have internal programs available for adaptation and training, development of soft skills and leadership abilities that are tailored individually to each employee;
  • We also provide partial compensation for employees participating in external training and conferences;
  • In tourism, it's difficult to grow without an excellent knowledge of English, and we support our employees' language learning goals — we organize group and individual lessons, plus speaking clubs with colleagues from all over the world;
  • And, of course, to encourage you to travel more, we offer corporate prices on hotels and other travel services;
  • We prioritize well-being and are committed to supporting the overall health and work-life balance at ETG. As part of this commitment, we provide MyTime Day Off - an extra day off that is designed to give our employees the flexibility to focus on important matters, whether it’s taking care of their health, mental recharge, addressing personal issues, or any other important activities.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.