Data Scientist

BreezoMeter is looking for a Data Scientist with the creativity, professionalism and passion to develop innovative data driven backend systems that impacts the lives of millions of users around the world.

In this position you’ll be working with exceptional people in a multidisciplinary R&D team to create inspiring data driven solutions that you can be proud of.

We are looking for people who are

  • Curious and enthusiastic to learn new things
  • Independent in their approach and learning
  • Team players
  • Thoughtful, positive and proactive
  • Flexible and possess a “can-do” attitude
  • Researchers in heart with strong mathematical intuition

Responsibilities

  • Identify data related / ML opportunities and own their end-to-end delivery, including research, model development, prototyping, offline validation, implement into production and online testing
  • Extend company’s data with third party sources of information when needed
  • Enhance data collection procedures to include information that is relevant for building analytic systems
  • Processing, cleansing, and verifying the integrity of data used for analysis
  • Work with fellow engineers from various disciplines and convey complex data related ideas in a concise and clear way
  • Take an active part in the ideation process, bringing to the table your deep understanding of data

Requirements

  • Minimum of 4 years full-time experience in Data Science positions
  • Academic degree in Computer Science / Math (or a related discipline) and very strong analytical skills
  • Extensive experience with data science tools (Jupyter Notebook, Pandas, scikit-learn etc.)
  • Excellent familiarity with Python / R
  • Familiarity with software development and design principles
  • Experience in integrating ML models into a large-scale production systems
  • Experience working with data pipelines and various data store types
  • Familiarity with one of the leading public cloud platforms (preferably Google Cloud Platform)
  • Bonus points:
    • Familiarity with common container orchestration & monitoring frameworks and tools (e.g. k8s, Prometheus)
    • Familiarity with modern ML lifecycle management frameworks (e.g. MLFlow)
    • Experience with Deep Neural Network libraries (e.g. TensorFlow, PyTorch)
    • Familiarity with Geographical Information Systems (GIS)