Hardware Data Analyst - Remote

Aperia is unlocking a new era of efficiency and sustainability for commercial vehicle fleets, by developing innovative hardware and data analytics solutions. Inventors of the award- winning and disruptive Halotm Automatic Tire Inflator, and the cloud delivered Halo Connect tire analytics platform. Aperia saves fleets thousands of hours of down-time every week, reduces the carbon footprint and environmental impact of the freight industry. Join us as we expand our product offering and take the next steps on our journey toward bringing the "backbone of our economy" into the automation age.

This position requires candidates to be authorized to work in the US on an ongoing basis without current or future employer sponsorship. We are unable to sponsor or transfer H-1B or other employment-based visas, and candidates currently on F-1, OPT, STEM OPT status are not eligible for this role.Your Role

As a Hardware Data Analyst, you will harness vast amounts of data streaming from our extensive deployment of telematics and IoT devices in the field. Your work will directly impact our hardware systems' performance, reliability, and efficiency, helping deliver exceptional customer experiences. Collaborating with engineering, product management, data science, and customer support teams, you will process and analyze sensor and analytics data to uncover insights, identify issues, and drive continuous improvement.

Your Responsibilities

• Design and deliver dashboards, reports, and presentations using tools like QuickSight, Tableau, or Python libraries to visualize performance metrics and communicate findings effectively.

• Continuously monitor and analyze incoming sensor and analytics data to identify trends, anomalies, and potential issues in the field.

• Use statistical models and machine learning techniques to extract insights and generate actionable recommendations for hardware reliability and performance improvements.

• Compare outputs from various firmware versions to validate improvements or detect unexpected regressions.

• Collaborate with hardware engineers, product managers, and data scientists to define and execute data analysis strategies.

• Support customer support teams by providing data-driven insights to address product performance and anomaly-related inquiries.

• Proactively identify and propose new tools, methods, or processes to enhance data analysis capabilities and improve operational efficiency.

• Collaborate with the engineering and data science teams to design and implement data pipelines that ensure reliable ingestion, processing, and storage of large-scale field data.

• Act as a data advocate within the organization, ensuring teams have the necessary data access and tools to make informed decisions.