Data Architect

Work Location: India

About the Lead Data Scientist (GenAI & NLP) Job Role

You will help our clients solve real-world problems by tracing the data-to-insights lifecycle:

• Understand business problems, making sense of the data landscape & footprint, performing a combination of exploratory, Machine Learning & Advanced Analytics on textual data.
• Create, experiment with, and deliver innovative solutions in a consultative mindset to client stakeholders using textual data

Qualification for Lead Data Scientist (GenAI & NLP)

• Background in Computer Science/Computer Applications or any quantitative discipline (Statistics, Mathematics, Economics/Operations Research, etc.) from a reputed institute.
• Total 4+ years of experience using analytical tools/languages like Python on large-scale data.
• Must have Semantic model & NER experience.
• Experience working with pre-trained models, awareness of state-of-the-art in embeddings, and applicability for use cases.
• Must have strong experience in NLP/NLG/NLU applications using popular Deep learning frameworks like PyTorch, Tensor Flow, BERT, Langchain, GPT (or similar models), and OpenCV.
• Must have exposure to Gen AI models (LLMs) like Mistral, Falcon, Llama 2, GPT 3.5 & 4, Prompt Engineering.
• Experience using Azure services for ML & GenAI projects is a plus.
• Demonstrated ability to engage with client stakeholders.
• Deep knowledge of techniques such as Linear Regression, gradient descent, Logistic Regression, Forecasting, Cluster analysis, Decision trees, Linear Optimization, and Text Mining.
• Strong understanding of integrating NLP models into business workflows. Prospects should be exposed to project initiation and business impact creation in at least one project.
• Broad knowledge of fundamentals and state-of-the-art in NLP and machine learning.
• Coding skills in one or more programming languages, such as Python and SQL.
• Hands-on experience with popular ML frameworks such as TensorFlow.
• Expert / high level of understanding of language semantic concepts & data standardization.