Are you an experience Senior Data Scientist with strong skills and experience with NLP?
As a Senior Data Scientist you will play an important role in advancing our natural language processing capabilities. We work on an Azure Stack, and in this role you will crucial in defining the required additional services to achieve the set outcomes. To be successful in this role, experience and expertise in the areas of NLP, segmentation models and applied ML predication and recommendation models is essential.
Salary: Competitive, this will be discussed should you proceed to phone screening stage.
Your responsibilities will include:
Design, develop, and fine-tune state-of-the-art NLP models to address complex language- related challenges.
Conduct rigorous evaluation and validation of language models, focusing on accuracy, robustness, efficiency and scalability.
Collaborate closely with data engineering to seamlessly integrate NLP solutions.
Stay on the cutting edge of NLP by exploring and implementing the latest advancements in large language models.
Ensure compliance with ethical considerations in NLP, particularly when working with AI systems that generate text.
Work towards user cases and enable desired outputs using ML and AI technologies using raw data across multiple sources.
Engage with key stakeholders and gather their requirements for new user cases.
The ideal candidate:
The ideal Senior Data Science candidate will possess:
Significant experience in a hands on data science, production environment, at least 6 years.
A strong foundation in natural language processing, with hands- on experience working with large language models, such as GTP’s and NLP frameworks like Transformers, VectorDBs.
Ability to utilize data science techniques to analyse data, detect patterns, extract and present insights with advance visualization.
Proficiency in any of the following languages, Python, Scala, R. Pyspark
Experience in fine tuning and adapting pre-trained language models to specific user bases.
Experience in working with user data enrichments across various data sources for clustering, prediction and recommendation solutions.
A degree in Computer Science (preferably with Machine Learning), Statistics, Math’s, or Engineering