Junior Data Analyst

You will work closely with the machine learning team to ensure that the data meets the necessary standards for effective model training.

Key Responsibilities:

Data Collection and Organization:

• Gather raw audio files in various formats (e.g., MP3, WAV, FLAC) from diverse sources such as interviews, podcasts, and YouTube videos.
• Organize files into a structured directory hierarchy, ensuring a clear and consistent file naming convention.

Audio Preprocessing:

• Convert audio files to the required format (16kHz mono, 16-bit signed integer WAV) using tools like FFmpeg.
• Transcribe audio files, either manually or through a transcription service, and store text files with corresponding filenames.

Data Cleaning and Normalization:

• Clean and normalize text data to address spelling variations, punctuation issues, and formatting inconsistencies.
• Standardize abbreviations and contractions, and remove special characters or unnecessary symbols.

Data Segmentation and Labeling:

• Split lengthy audio recordings into smaller, manageable segments.
• Create and maintain a metadata file that maps audio files to their corresponding transcriptions and alignment details.

Quality Assurance and Validation:

• Conduct thorough quality checks to validate the dataset for accuracy, consistency, and completeness.
• Identify and resolve issues in the audio and text data, such as misalignments or incorrect transcriptions.

Data Analysis and Reporting:

• Use data analysis techniques to evaluate dataset health and completeness.
• Provide regular reports on data collection progress, challenges, and recommendations for improvements.

Collaboration and Communication:

• Work closely with the machine learning team to address any data-related issues.
• Provide regular updates on data collection and preprocessing progress.

Qualifications:

• Strong Proficiency in Python: Experience with data manipulation, cleaning, and preprocessing using Python libraries such as Pandas, NumPy, and TensorFlow.
• Data Cleaning and Preprocessing: Proven ability to clean, organize, and preprocess data for machine learning applications.
• NLP Knowledge: Familiarity with natural language processing techniques, including text normalization and handling multilingual or code-mixed data.
• SQL Skills: Experience with SQL for data querying and management.
• Problem-Solving Skills: Ability to identify and solve complex data-related problems with creativity and efficiency.
• Work Under Pressure: Capable of handling multiple tasks simultaneously and meeting deadlines in a fast-paced environment.
• Adaptability: Willingness to learn new tools and techniques as needed for the project.
• Attention to Detail: Meticulous attention to detail to ensure data accuracy and integrity.
• Communication Skills: Excellent communication skills to collaborate effectively with cross-functional teams.

Desired Skills:

• Familiarity with audio processing tools like FFmpeg.
• Familiarity with transcription tools and alignment software (e.g., Aeneas, Gentle).
• Knowledge of Taglish language nuances and variations.
• Experience with version control systems like Git.
• Familiarity with code-mixing or multilingual NLP techniques