Description

Key Skills: Forecasting, Chatbots, Predictive Modelling, LLM, Time Series Analysis, Data modeling, Deployment

Roles and Responsibilities:

• Develop and deploy machine learning models for time-series forecasting, classification, regression, stock scoring, and portfolio analysis.
• Perform feature engineering using financial, market, and alternative data.
• Evaluate models using statistical and financial performance metrics, ensuring interpretability for decision-making.
• Design and build LLM-powered chatbots for investment insights and customer support, implementing prompt engineering techniques.
• Build Retrieval-Augmented Generation (RAG) frameworks and design Agentic AI architectures for collaborative tasks.
• Deploy models using MLOps best practices, ensuring scalability and monitoring.
• Collaborate with backend and frontend teams to integrate AI services via APIs.
• Work closely with product managers to translate business requirements into AI solutions.
• Stay updated with trends in quantitative finance, machine learning, and generative AI.

Skills Required:

• Strong experience in predictive modeling and statistical analysis
• Proficiency in machine learning frameworks (scikit-learn, TensorFlow, PyTorch, etc.)
• Experience in time-series forecasting and financial data modeling
• Knowledge of LLMs, RAG frameworks, and Agentic AI architectures
• Hands-on experience with chatbot development and prompt engineering
• Understanding of MLOps practices for deployment and monitoring
• Ability to perform feature engineering using financial, market, and alternative data
• Proficiency in Python, SQL, and relevant data science tools
• Strong problem-solving, analytical, and quantitative skills
• Ability to collaborate with cross-functional teams (backend, frontend, product managers)
• Awareness of trends in quantitative finance, AI, and generative AI

Education: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field

Key Skills

Education

Any Graduate