Introduction
We are seeking a Data-Centric AI Specialist who focuses on strengthening AI systems through high-quality data. If you are passionate about data labeling, annotation, curation, and bias reduction, this role offers the opportunity to shape how AI models are built, evaluated, and deployed responsibly.
As a Data-Centric AI Specialist at our organization, you will work across AI development pipelines to enhance data preparation and validation. You’ll collaborate with machine learning engineers, data engineers, and domain experts to ensure AI models achieve peak performance through superior data practices.
We offer competitive compensation, comprehensive benefits, and opportunities to work on transformative AI projects that rely on data excellence.
Objectives of this role
- Ensure AI models are trained with high-quality, diverse, and bias-mitigated datasets.
- Develop frameworks for data collection, labeling, and curation at scale.
- Collaborate with teams to prioritize data improvements over model complexity.
- Research and apply emerging practices in data-centric AI.
Your tasks
- Audit datasets for quality, bias, and representativeness.
- Implement labeling workflows and quality assurance processes.
- Automate data cleaning, preprocessing, and augmentation pipelines.
- Collaborate with ML engineers to identify data-driven performance improvements.
- Monitor dataset health and establish KPIs for data quality.
- Contribute to open-source or industry initiatives on data-centric AI.
Required skills and qualifications
- Bachelor’s degree in Computer Science, Data Science, or related field.
- Experience with data engineering, curation, or annotation pipelines.
- Strong programming skills in Python and familiarity with ML workflows.
- Understanding of data bias, fairness, and ethical AI practices.
- Experience with large-scale dataset management and validation.
Preferred skills and qualifications
- Advanced degree in AI, Data Engineering, or Computational Statistics.
- Familiarity with active learning, weak supervision, and data programming.
- Experience with crowdsourcing and human-in-the-loop labeling platforms.
- Research contributions in data-centric AI or responsible ML.
- Knowledge of compliance frameworks related to data use (GDPR, HIPAA).
