Alex Staff Agency

About the company and the project:

A data-driven company operating in the building energy efficiency sector, founded in 2023. The company helps hotels and other energy-intensive businesses reduce energy consumption by 10-20% using data collection, smart analytics, and digital twins.

Their platform integrates with building systems to detect inefficiencies — such as pool pumps running unnecessarily or mismatches between HVAC operation and occupancy — helping clients reduce costs by up to 30% and lower emissions.

They also support carbon footprint tracking (Scope 1, 2, 3), ensure compliance with sustainability standards (CSRD, ESG, B Corp, Green Key), and enable automated reporting.

You are offered a remote position based in Cyprus.

Your tasks on the position:

  • Develop ML models to detect energy inefficiencies from smart meter data
  • Forecast energy consumption and optimize HVAC system performance
  • Build internal analytics workflows and scalable data pipelines
  • Collaborate with domain experts to turn insights into real-world actions
  • Explore and implement advanced ML/DL methods to drive innovation

Requirements:

  • 4+ years of experience in Data Science, ideally in energy, IoT, or smart buildings
  • Strong skills in Python, SQL, and time-series modeling (e.g., ARIMA, LSTM, Prophet)
  • Experience with machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch)
  • Familiarity with large datasets, cloud environments (e.g., GCP), and model deployment
  • Excellent communication and data visualization skills
  • Master’s or Ph.D. in a quantitative field (e.g., Math, Statistics, Computer Science)
  • Azure experience
  • AWS experience
  • English C1+

Benefits:

  • Competitive salary from your competencies
  • Full-time remote position with occasional travel
  • Official employment in Cyprus, fully compliant with local labor laws
  • The role includes paid vacation and sick leave, aligned with standard practices for full-time positions
  • Stable, fast-growing company with a flat organizational structure

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