Square Sense is developing products to understand people’s behavior and building performance using data collected from sensors and systems within a building. Our models infer knowledge using data from heating, ventilation, and air conditioning (HVAC) systems, lighting, smart meters, Wi-Fi, people counting & occupancy sensors, access control systems, elevators, air quality sensors. These models are based on statistics, probabilistic models, machine learning, and deep learning. The understanding of how a building is used and how it performs under various conditions is used to devise and implement strategies to reduce operating costs, improve the user experience, and increase revenue. Our goal is to develop decision-making support systems and autonomous optimization routines, both at the building level and at the portfolio level (i.e., for clients that own several buildings).
Data analysts at Square Sense are responsible for developing products to support decision-making from available data. For such purpose, they develop client-facing dashboards and work with data of various kinds and from multiple buildings. The tasks of a data analyst include quantifying relationships between different variables under various circumstances (e.g., actual or projected), and identifying trends and patterns. They work with the data produced by our data processing pipeline, which is mainly developed and maintained by our engineering and research departments, with whom analysts interact for its design. The data pipeline is responsible for collecting sensor data, transform it using various algorithms and models, and store the results into an analytical database. Data analysts use business intelligence software such as Power BI as well as Python-based tools (Dash, Plotly, Matplotlib) to prepare reports.
Resume interview, company and position presentation (phone/video call, 25 minutes)
Technical assessment: home assignment plus 2-hour interview (video call)
Fit interviews with various members of the team (face-to-face or video call)
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