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Data Scientist

3 - 5 years

3 - 7 Lacs

Posted:6 hours ago| Platform: Naukri logo

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Job Description

Job Summary We are looking for an experienced Data Scientist with 3 to 5 years of hands-on experience in building and deploying machine learning and natural language processing (NLP) models. The ideal candidate should have a solid foundation in AI, strong problem-solving abilities, and a passion for applying ML/NLP techniques to solve real-world challenges. Experience in designing AI agents or working with LLMs is a strong advantage. Key Responsibilities Develop, train, and deploy machine learning and NLP models to support business and product goals. Conduct end-to-end model lifecycle management, including feature engineering, model selection, evaluation, and optimization. Perform exploratory data analysis (EDA), hypothesis testing, and derive actionable insights. Design and build AI agents and workflows that interact with LLMs and integrate into larger systems. Collaborate with cross-functional teams including product, engineering, and design to deliver AI-driven solutions. Stay updated with advancements in GenAI, LLMs, and emerging trends in the AI ecosystem. Required Qualifications Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. 3–5 years of experience in applied machine learning, NLP, or AI model development. Proficiency in Python and commonly used data science libraries (e.g., Pandas, NumPy, Scikit-learn, Transformers). Hands-on experience with NLP frameworks (e.g., Hugging Face, spaCy) and model fine-tuning. Strong understanding of supervised/unsupervised learning, vectorization techniques, and evaluation metrics. Excellent problem-solving, communication, and collaboration skills. Preferred Qualifications Experience working with large language models (LLMs) and vector databases. Exposure to building AI agents, autonomous workflows, or prompt engineering. Familiarity with cloud platforms (e.g., AWS) and scalable ML infrastructure. Understanding of MLOps practices for model deployment and monitoring.

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