Time-Series Signal Processing and Data Analyst Intern

Renesas Electronics•Columbia, MD

2h•Onsite

About The Position

Renesas is an embedded semiconductor solution provider driven by its Purpose ‘To Make Our Lives Easier.’ As the industry’s leading expert in embedded processing with unmatched quality and system-level know-how, we have evolved to provide scalable and comprehensive semiconductor solutions for automotive, industrial, infrastructure, and IoT industries based on the broadest product portfolio, including High Performance Computing, Embedded Processing, Analog & Connectivity, and Power. With a diverse team of over 22,000 professionals in more than 30 countries, we continue to expand our boundaries to offer enhanced user experiences through digitalization and usher into a new era of innovation. We design and develop sustainable, power-efficient solutions today that help people and communities thrive tomorrow, ‘To Make Our Lives Easier.’ We are seeking a proactive, passionate, and detail-oriented Time-Series Signal Processing and Data Analyst Intern to join our dynamic team. This role focuses on sound data collection with e-bikes, documenting and organizing the data, and applying signal processing and AI algorithms. The ideal candidate is enthusiastic about learning, problem-solving, and collaborating on impactful real-world projects that integrate data processing, visualization, and scripting.

Requirements

Must be enrolled full time as a rising junior or senior bachelor students in Computer Science, Mechanical Engineering, Electrical Engineering, or a similar field.
Must not have graduated before September 2026.
Proficiency in Python (NumPy, Pandas, Matplotlib/Plotly, Scikit-learn) and MATLAB, with experience writing clean, modular, and efficient code.
Ability to work with large-scale datasets, including merging, cleaning, and preprocessing data from multiple sources.
Basic understanding of time-series signal processing (e.g., sampling, FFT, STFT, spectrogram).
Basic or willing to learn knowledge of audio recordings and related processing techniques.
Experience with machine learning algorithms (SVM, logistic regression, neural networks, convolutional networks).
Experience with code documentation and effective presentation skills.
Familiarity with workflow automation and effective data visualization techniques.
Valid driving license for data collection.
Familiarity with data collection and data acquisition systems.
Strong problem-solving skills, attention to detail, and organizational abilities.
Eager to learn and passionate about developing efficient code and workflows for large-scale data analysis.

Responsibilities

Analyze the sound data through signal processing, statistical and AI algorithms.
Develop tools for data visualization to streamline data selection and splitting.
Perform data collection activities and contribute to experimental setups.
Ensure all data is collected/organized/store properly
Document workflows, assumptions, and updates to ensure clarity, reproducibility, and maintainability.