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Language Data Scientist

Job in Toronto, Ontario, M5A, Canada
Listing for: Innodata Inc
Full Time position
Listed on 2026-02-28
Job specializations:
  • IT/Tech
    Data Scientist, AI Engineer, Data Analyst, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 120000 CAD Yearly CAD 120000.00 YEAR
Job Description & How to Apply Below
Overview

Job Title:

Language Data Scientist

Location:

Remote within Canada (excluding Quebec)

Employment Type:

Full-Time (40 hours per week) Fixed-Term
About Innodata  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.
Qualifications   Knowledge of how components of GenAI products or services combine to work.
Collaborating with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.
MA in (computational) linguistics, data science, computer science (AI / ML / NLU), quantitative social sciences or a related scientific / quantitative field;
PhD strongly preferred.
Language and language data expertise:  Extensive experience working with human language data and designing human evaluation tasks, including multi-phase and complex workflows.
Deep understanding of language and its relationship with culture.
Ability to identify ambiguity and subjectivity in language.
Ability to work with multilingual and multi-modal projects.
Quantitative Analysis

Skills:

Advanced knowledge of statistics, metrics (e.g., F1 score, inter-rater reliability metrics), and data analysis methods such as sampling.
Technical skills:  

Experience with NLP techniques and tools, such as Spa Cy, NLTK, or Hugging Face. Proficiency in Python to handle/transform large datasets (e.g., pre- and post-processing data, pandas), perform quantitative analyses, and visualize data (e.g., matplotlib, seaborn).
Data processing:  Deep understanding of data pipelines to support ML and NLP workflows; knowledge of efficient data collection, transformation, and storage; knowledge of data structures, algorithms, and data engineering principles.
Excellent interpersonal skills for effective cross-functional stakeholder engagement.
Excellent problem-solving skills, with the ability to think critically and creatively to develop innovative AI solutions.
Ability to work independently and collaborate as part of a team.
Adaptable to changing technologies and methodologies.
Ability to translate experience, research and development information to understand client products and services.

Preferred…
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