Why Vacasa

As a Machine Learning Team Lead at Vacasa, Prague you'll lead a newly emerging team which has an impact on maximizing company's revenue and efficiency. Vacasa's ML and Data Science initiatives span various areas, including dynamic pricing and probability models, with opportunities to explore recommender systems, NLP, and neural networks using extensive data resources.

Key Projects Prague AI team is working on: 

Development and Enhancement of DS Platform: Spearhead the development and enhancement of our Data Science (DS) platform, which serves as the central hub for stakeholders and the Data Science team. This platform hosts all DS models in the form of APIs, ensuring accessibility and usability for all stakeholders.

Integration of GenAI and Dynamic Pricing Models: Lead the integration of GenAI models like chatting applications, document search, dynamic listings generation. Second major challenge is the Dynamic Pricing project. This integration involves not only incorporating the models into the production but also optimizing their code for maximum efficiency and performance. Particularly, the Dynamic Pricing project, characterized by its extensive data volume processing over 2.5 billion records daily.

What you'll do

  • Lead, mentor, and grow a team of Machine Learning Engineers
  • Define repeatable architectural patterns for large-scale, adaptive, secure, performant systems.
  • Select technologies, guide technical solutions, and produce high quality documentation.
  • Productionize machine learning models using AWS, Python 3, CI/CD, Airflow, and Terraform.
  • Write maintainable, reliable, and robust pipelines complete with unit and integration tests.
  • Contribute to all phases of development and delivery.
  • Develop dashboards to monitor pipeline health, and alert on key metrics.
  • Present results to stakeholders and executives as needed
  • Conduct code reviews, peer design, debug difficult problems across multiple projects, and become an expert in MLOps.

Skills you'll need

  • Software engineering or data science, including management experience
  • Excellent communication in English 
  • Ability to navigate bigger organization with engineering spread across numerous time zones
  • Familiarity with Python and RDBMS SQL
  • Familiarity of modern generative AI techniques and machine learning algorithms, including supervised, unsupervised, neural network, regression, and tree-based approaches.
  • Expert skills in cloud service architecture (AWS, Azure, GCP)
  • Familiarity with AWS tools such as SageMaker, EMR (Spark), Lambda, and Fargate a plus.
  • Experience with service-oriented architecture and scaling large-scale systems.
  • Experience writing infrastructure as code, Terraform, and Docker is a plus.
  • Ability to interact with technical and business stakeholders to solicit necessary requirements for implementation and execution of data science projects

What you’ll get

  • 5 weeks of vacation
  • 12 sick days 
  • Meal allowance 
  • Contribution for pension insurance
  • Hybrid work and flexible working hours
  • Fresh fruits and snacks in the office
  • Employee Assistance Program
  • Career development opportunities
  • Employee discounts 
  • All the equipment you’ll need to be successful
  • Modern offices in Prague - Karlín

Vacasa is an equal opportunity employer committed to fostering a diverse and inclusive workplace. We do not discriminate against applicants based upon race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, or other classes protected by applicable law. Veterans are encouraged.

Vacasa is committed to maintaining a safe and productive work environment. Possession, use, or being under the influence of alcohol or illegal drugs in the workplace is prohibited.

An offer of employment for this role will be contingent upon the successful completion of a background check and/or OFAC  screening.

Location

Prague, CZ

Job Overview
Job Posted:
6 days ago
Job Expires:
Job Type
Full Time

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