NLP Data Scientist

As an NLP Data Scientist at MyndYou, a leader in AI-driven healthcare technology, you will play a critical role in developing intelligent language-based systems that directly support our core mission. You’ll lead efforts in designing, evaluating, and deploying state-of-the-art Natural Language Processing (NLP) and Large Language Model (LLM) capabilities in production. From prompt engineering and NLU model development to model optimization and monitoring, your work will impact real-world outcomes and shape the future of digital health solutions. This role bridges applied machine learning, linguistics, and GenAI innovation—ideal for those who want to build solutions that matter.

Who You Are:

You are an experienced, impact-oriented NLP expert with a strong foundation in machine learning and a deep curiosity for how language models behave. You are confident working independently or collaboratively and thrive in a fast-paced environment. You understand the nuances of building robust NLP systems for production and have a growing or deepening interest in prompt engineering, LLM evaluation, and answer extraction. You care about system quality, safety, and user experience as much as performance metrics.

What You’ll Do:

Develop and maintain Natural Language Understanding (NLU) models integrated into key company products.

Design and iterate on prompt templates and strategies using techniques such as few-shot, zero-shot, and chain-of-thought prompting.

Apply best practices from prompt engineering, including answer engineering (e.g., extractors, verbalizers, formatting).

Evaluate model outputs using modern benchmarking techniques (e.g., self-consistency, prompt ensembles).

Collaborate with cross-functional teams (engineering, product, clinical) to tailor LLM-based systems to practical use cases.

Monitor and improve model performance post-deployment with a focus on stability, safety, and domain alignment.

Contribute to the internal prompt engineering knowledge base and stay up to date with the latest research and community best practices.