AI Data Collection Expert - 1+/Walk-in

About the role:

AI Data Collection Expert

Key responsibilities:

• Data sourcing and acquisition: Collaborate with AI/ML researchers and data scientists to understand data requirements and execute comprehensive data acquisition strategies.
• Dataset curation and management: Gather, curate, and maintain large-scale datasets, including text, images, audio, and video, from various sources such as websites, databases, and APIs.
• Data labeling and annotation: Use specialized tools and platforms to accurately label and annotate diverse datasets for supervised learning models, ensuring clarity and consistency.
• Quality assurance and validation: Implement and manage robust quality control frameworks to audit datasets, identify inconsistencies or inaccuracies, and maintain a high data acceptance rate.
• Process automation: Identify opportunities to automate and optimize data collection pipelines and labeling workflows to improve efficiency and scalability.
• Collaboration: Work closely with cross-functional teams, including product managers and software engineers, to define data requirements and integrate data collection processes into broader product initiatives.
• Documentation: Create and maintain detailed documentation of data sources, collection methodologies, and quality assurance processes for transparency and reproducibility.
• Compliance and ethics: Ensure all data collection activities comply with relevant data privacy and security regulations (e.g., GDPR, CCPA) and ethical AI principles.

Required qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related technical field.
• Technical experience: Proven experience in data collection, data engineering, or data annotation, preferably within an AI/ML context.
• Programming proficiency: Strong skills in scripting languages like Python and experience with SQL.
• Tool familiarity: Experience with data collection tools (e.g., web scraping libraries like Scrapy or Selenium) and data annotation platforms (e.g., Labelbox, Amazon SageMaker Ground Truth).
• Cloud experience (preferred): Experience with cloud data services on platforms like AWS, GCP, or Azure.
• Problem-solving: Exceptional analytical and problem-solving abilities with an unwavering commitment to data quality.
• Attention to detail: Meticulous attention to detail is critical for ensuring the accuracy and integrity of datasets.
• Communication: Excellent written and verbal communication skills, with the ability to effectively document processes and collaborate with technical and non-technical stakeholders.

Preferred qualifications:

• Previous Data collection experience of 1+ years.