Lead Data Scientist

Responsibilities:

• Lead the development and deployment of NLP-based solutions to process and analyze unstructured data at scale.
• Design, train, and optimize machine learning models using libraries such as PyTorch, NLTK, and Scikit-learn.
• Architect and deploy AI/ML products on cloud platforms like Azure, GCP, or AWS.
• Collaborate with data engineering teams to ensure seamless integration of AI models into production systems.
• Perform advanced SQL analytics to extract actionable insights from structured datasets.
• Stay up-to-date with the latest advancements in NLP and machine learning techniques.
• Mentor junior data scientists and foster a culture of technical excellence within the team.
• Communicate complex technical concepts to non-technical stakeholders and customers.
• Partner with customers to understand their needs and translate them into technical solutions.
• Work with large-scale datasets using Python, SQL, Spark, Databricks, or cloud data platforms.
• Build ETL/ELT pipelines, feature stores, and model-serving infrastructures.
• Deploy ML models into production environments using Docker, Kubernetes, and CI/CD pipelines.
• Implement monitoring, observability, and retraining workflows.
• Mentor junior data scientists and provide technical oversight for AI/ML projects.
• Collaborate with cross-functional teams to define model requirements and success metrics.
• Own the full ML lifecycle from research to deployment and ongoing maintenance.
• Develop, train, and optimize ML and deep learning models (classification, regression, clustering, sequence modeling, and embeddings).
• Implement and fine-tune transformer-based models such as BERT, GPT-style LLMs, and domain-specific architectures.
• Build and deploy RAG (Retrieval Augmented Generation) pipelines, vector databases, embedding models, and prompt optimization workflows.
• Fine-tune pretrained LLMs and Hugging Face models for domain-specific tasks.
• Develop custom tokenizers, embeddings, and text-processing architectures.
• Build, evaluate, and deploy GenAI models for text generation, document processing, knowledge retrieval, and agent-based automation.
• Integrate LLMs into production systems using APIs, LangChain, LlamaIndex, or custom frameworks.
• Design safety, evaluation, and monitoring processes for GenAI deployments.

Requirements:

• Minimum 6 years of experience in data science, with a focus on NLP and unstructured data processing.
• Proven track record of launching NLP-driven products to live users.
• Expertise in Python and standard libraries such as PyTorch, NLTK, and Scikit-learn.
• Experience with transformer-based models (e. g., BERT, GPT).
• Strong experience with one or more cloud platforms (Azure, GCP, AWS) for hosting and deploying AI/ML products.
• Design and implement NLP pipelines for text classification, information extraction, topic modeling, semantic search, summarization, and conversational AI applications.
• Familiarity with data engineering pipelines and best practices.
• Proficiency in SQL for analytics and data manipulation.
• Excellent problem-solving skills and ability to work with large-scale datasets.
• Strong interpersonal and communication skills, with the ability to mentor team members and interact with customers effectively.