Language Data Scientist
Confidential
Posted: March 6, 2026
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Quick Summary
A Language Data Scientist is responsible for analyzing and developing AI models to extract insights from large datasets, with a focus on data quality and accuracy.
Required Skills
Job Description
Job Title: Language Data Scientist
Location: Fully Remote within the U.S. (excluding California, Washington, Alaska, Colorado, Montana, New York, Puerto Rico, Nevada, Nebraska)
Employment Type: Full-Time (40 hours per week) Fixed-Term
Who we are:
Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.
By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.
About the Role:
Innodata is building a team of Language Data Scientists and Gen AI experts to help our customers advance GenAI applications. You will work hands-on with multi-modal and multi-lingual datasets and collaborate with cross-functional partners. You will use your experience with human and synthetic data workflows to drive innovation and continuous improvement. The ideal candidate must have the right mix of skills in (computational) linguistics and human evaluation tasks, data science, and data engineering.
Key Responsibilities:
• Design/improve workflows to create data for AI/ML training and evaluation. Includes human annotation and data collection workflows, as well as synthetic ones.
• Dive deep into existing workflows and processes to gather data and insights, make recommendations, and drive improvement through innovation and cross-functional collaboration with customers
• Critically assess annotation tooling and workflows
• Quantitatively analyze large datasets, perform statistical analysis, calculate metrics, and make recommendations to improve accuracy and performance
• Work closely with client stakeholders on understanding goals, gathering requirements, proposing solutions and executing them.