Job Title

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 inPython or MATLABfor data analysis, visualization, and reporting. Strong foundation intime-series analysis , signal processing, and statistical modeling (e.g., autocorrelation, moving averages, seasonal decomposition). Experience working withMEMS sensorsand sensor data acquisition systems. Hands-on experience withpandas, NumPy, SciPy, scikit-learn, and matplotlib . Ability to developautomated KPI reportsand interactive dashboards (Power BI or Tableau). Preferred Qualifications Prior experience with data fromsmartphones, hearables, or wearable devices . Advanced knowledge inMEMS sensor data wranglingtechniques. Familiarity withcloud platformssuch as AWS, Azure, or Google Cloud Platform. Exposure toreal-time data streamingand processing frameworks/toolboxes.