Data Scientist
Dataeaze • Hyderabad
About the Role The Data Scientist will be responsible for analyzing large, complex datasets to identify trends, patterns, and actionable business insights. The role involves designing, developing, and deploying machine learning models, including classification, regression, clustering, recommendation systems, and other predictive analytics solutions. Key Responsibilities: Analyze large, complex datasets to identify trends, patterns, and actionable business insights. Design, develop, and deploy machine learning models including classification, regression, clustering, recommendation systems, and other predictive analytics solutions. Perform data cleaning, feature engineering, exploratory data analysis (EDA), and data preprocessing. Build and maintain predictive, statistical, and AI-driven models to address business challenges and improve decision-making. Develop and implement Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge bases and unstructured data sources. Design and develop Generative AI applications using LangChain and LangGraph for workflow orchestration, agentic AI, and LLM-powered solutions. Collaborate with stakeholders to understand business requirements and translate them into scalable analytical and AI-driven solutions. Develop dashboards, reports, and visualizations to communicate insights effectively to technical and non-technical audiences. Monitor model performance, conduct model tuning, and optimize solutions for accuracy, scalability, and efficiency. Requirements: 4–5 years of professional experience as a Data Scientist or in a similar role. Strong proficiency in Python and related libraries such as NumPy, Pandas, Scikit-learn, etc. Hands-on experience in the end-to-end Data Science model development lifecycle, including data collection, data preprocessing, feature engineering, exploratory data analysis (EDA), model development, validation, deployment, monitoring, and optimization. Strong expertise in Machine Learning, including supervised and unsupervised learning techniques. Practical experience and strong conceptual understanding of Deep Learning and Natural Language Processing (NLP). Hands-on experience with Generative AI (GenAI) technologies, specifically Retrieval-Augmented Generation (RAG), LangChain, and LangGraph. Candidates must possess both practical implementation experience and strong conceptual knowledge of Machine Learning, Deep Learning, NLP, RAG, LangChain, and LangGraph. Experience with data visualization tools such as Tableau, Power BI, Matplotlib, or Seaborn. Strong hands-on experience with SQL and relational databases. Knowledge of model evaluation techniques, performance metrics, and model optimization. Strong analytical, problem-solving, and critical-thinking abilities. Excellent communication and presentation skills, with the ability to explain complex concepts to non-technical stakeholders. Preferred Qualifications: Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras. Experience with cloud platforms including AWS, Azure, or GCP. Knowledge of MLOps, model deployment, CI/CD pipelines, and productionizing AI/ML solutions. Experience building AI agents, multi-agent systems, or workflow automation using LangGraph and related frameworks.
