Language Data Scientist

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 the AI technology solutions provider-of-choice to 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 clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 3,000 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.
Position Summary
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.
Who We’re Looking For
You have at least 3 years of relevant experience with data creation, curation and analysis for GenAI applications (e.g. RAG, Agents, complex reasoning). You are an expert in designing collection, evaluation and quality assurance processes, using human-in-the-loop and synthetic techniques. You bring a wealth of expertise in language, culture, and multi-lingual projects. You are experienced in analyzing data with advanced statistical tools and driving success through process excellence.
Your understanding of machine learning, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) help you tackle challenges with a critical, innovative mindset. You’re also a strong communicator, excelling in cross-functional collaboration and understanding business needs.
Tell Me More
As a Language Data Scientist, you create and own processes for creating, validating and annotating data for use in LLM/ML applications. This can be natural language data or multimodal data including images, video, audio and others. You consult and engage with customers to understand their business goals and design processes to meet them. You generate insights about the client’s processes and products to drive improvement and innovation. You advise and support business unit heads on engaging with customers to understand the upstream activities that would be performed using Innodata Inc services.
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.
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 multi-lingual 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 Natural Language Processing (NLP) techniques and tools, such as SpaCy, NLTK, or Hugging Face.
Proficiency in Python to:
handle / transform large datasets (e.g. pre- and postprocessing data, pandas)
perform quantitative analyses
visualize data (for example 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 Skills

Conducting research to stay up-to-date with the latest advancements in generative AI, machine learning, and deep learning techniques
Knowledge of optimizing existing generative AI models for improved performance, scalability, and efficiency
Experience of developing and maintaining ML/AI pipelines, including data preprocessing, feature extraction, model training, and evaluation
Model Fine-Tuning: Knowledge of Fine-tuning pre-trained models to adapt them to specific tasks and datasets, improving their performance and relevance
Developing clear and concise documentation, including technical specifications, user guides, and presentations, to communicate complex AI concepts to both technical and nontechnical stakeholders
Contributing to establishing best practices and standards for generative AI development with customers and within the organization
Providing technical mentorship and guidance to junior team members
Understanding of techniques such as GPT, VAE, and GANs
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