Data Scientist

Responsibilities

• Develop, deploy, and fine-tune LLMs and work on cutting-edge GenAI applications
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines
• Apply traditional machine learning techniques for scoring engines and decision support systems
• Design, execute, and iterate on rapid Proof-of-Concepts (POCs)
• Work with Python and SQL for data analysis, manipulation, and model development
• Leverage AWS ecosystem (Bedrock, SageMaker, EC2, Lambda) for scalable model training and deployment
• Handle NLP tasks including entity recognition, sentiment analysis, and text generation
• Collaborate with cross-functional teams to integrate ML solutions into fintech products

Requirements

• Exposure to building Agentic RAG and Graph RAG pipelines
• Solid understanding of machine learning algorithms and foundations
• Understanding of the ML lifecycle: training, deploying, and monitoring
• Proficiency in TensorFlow, PyTorch, Scikit-learn, Langchain, or LlamaIndex
• Knowledge of traditional algorithms like Linear/Logistic Regression, Random Forest, XGBoost, and ARIMA
• Understanding of NLP, LLMs, and VLMs (Llama, Anthropic)
• Ability to write and debug complex SQL queries
• Comfort with AWS ecosystem (EC2, Lambda, Bedrock, Cloudwatch)
• Bachelor's degree in Computer Science, Statistics, Mathematics, or Engineering
• 1 - 5 years of experience

Preferred Qualifications

• Master's degree in a quantitative discipline
• Prior experience in the fintech domain
• Experience with Docker, Flask, MLFLOW, Airflow, or K8s

About the Company

Easebuzz offers the opportunity to work on impactful data projects in the fintech domain within a collaborative and growth-oriented work culture.