Job Overview

experience: 5-6 years

key responsibilities

• process, analyze, and interpret time-series data from mems sensors (e.g., accelerometers, gyroscopes, pressure sensors).
• develop and apply statistical methods to identify trends, anomalies, and key performance metrics.
• compute and optimize kpis related to sensor performance, reliability, and drift analysis.
• utilize matlab toolboxes (e.g., data cleaner, ground truth labeler) or python libraries for data validation, annotation, and anomaly detection.
• clean, preprocess, and visualize large datasets to uncover actionable insights.
• collaborate with hardware engineers, software developers, and product owners to support end-to-end data workflows.
• convert and format data into standardized schemas for use in data pipelines and simulations.
• generate automated reports and build dashboards using power bi or tableau.
• document methodologies, processes, and findings in clear and concise technical reports.

required qualifications

• proficiency in python or matlab for data analysis, visualization, and reporting.
• strong foundation in time-series analysis, signal processing, and statistical modeling (e.g., autocorrelation, moving averages, seasonal decomposition).
• experience working with mems sensors and sensor data acquisition systems.
• hands-on experience with pandas, numpy, scipy, scikit-learn, and matplotlib.
• ability to develop automated kpi reports and interactive dashboards (power bi or tableau).

preferred qualifications

• prior experience with data from smartphones, hearables, or wearable devices.
• advanced knowledge in mems sensor data wrangling techniques.
• familiarity with cloud platforms such as aws, azure, or google cloud platform.
• exposure to real-time data streaming and processing frameworks/toolboxes.