AI Data Scientist

Role overview Research, build, evaluate, and deploy natural language processing and machine learning capabilities for software handling complex commercial documents and workflows. This hands-on role spans applied research through production delivery, with a focus on choosing approaches that fit the data, product needs, performance expectations, and operating costs.

Responsibilities - Investigate emerging NLP, machine learning, deep learning, and generative AI methods, testing their practical value through experiments. - Design models and data workflows, including preprocessing, features, embeddings, and training datasets for structured and unstructured information. - Build and evaluate language model solutions using techniques such as retrieval-augmented generation, prompting, fine-tuning, vector search, and structured outputs. - Select and optimize models against accuracy, latency, cost, and business requirements. - Develop benchmarks, evaluation datasets, and statistical methods; use error analysis to guide improvements. - Work with product, engineering, and data science teams to move solutions from early experiments into production, and help strengthen team practices through mentorship and evaluation standards.

Requirements - The source describes a technical leader who can combine research skills with practical implementation and production delivery. - Ability to independently investigate unclear problems, form hypotheses, and compare alternative approaches. - Expertise across NLP and machine learning methods, including deep learning and transformer-based systems. - Experience developing and evaluating generative AI or large language model solutions. - Ability to assess model quality and system tradeoffs, including accuracy, speed, and cost. - The source is truncated before the full required qualifications, so specific degree, years-of-experience, and tool requirements are not available.

Nice to have - Experience with efficient fine-tuning, quantization, distillation, inference optimization, synthetic data, or agent-based architectures. - Knowledge of cloud AI platforms and distributed model training or inference; contributions through publications, patents, open-source work, talks, or technical writing. - Experience in Agile, cross-functional product teams.

Benefits and work setup - Houston hybrid or Canada remote. Limited travel to other offices may be requested.