Lead Data Scientist

As a Machine Learning Engineer at our company, you will be responsible for designing and developing a cutting-edge application powered by large language models (LLMs) to provide market analysis and generate high-quality, data-driven periodic insights. Your role will be crucial in building a scalable and intelligent system that integrates structured data, NLP capabilities, and domain-specific knowledge to produce analyst-grade content. **Key Responsibilities:** - Design and develop LLM-based systems for automated market analysis. - Build data pipelines to ingest, clean, and structure data from multiple sources (e.g., market feeds, news articles, technical reports, internal datasets). - Fine-tune or prompt-engineer LLMs (e.g., GPT-4.5, Llama, Mistral) to generate concise, insightful reports. - Collaborate closely with domain experts to integrate industry-specific context and validation into model outputs. - Implement robust evaluation metrics and monitoring systems to ensure quality, relevance, and accuracy of generated insights. - Develop and maintain APIs and/or user interfaces to enable analysts or clients to interact with the LLM system. - Stay up to date with advancements in the GenAI ecosystem and recommend relevant improvements or integrations. - Participate in code reviews, experimentation pipelines, and collaborative research. **Qualifications Required:** - Strong fundamentals in machine learning, deep learning, and natural language processing (NLP). - Proficiency in Python, with hands-on experience using libraries such as NumPy, Pandas, and Matplotlib/Seaborn for data analysis and visualization. - Experience developing applications using LLMs (both closed and open-source models). - Familiarity with frameworks like Hugging Face Transformers, LangChain, LlamaIndex, etc. - Experience building ML models (e.g., Random Forest, XGBoost, LightGBM, SVMs), along with familiarity in training and validating models. - Practical understanding of deep learning frameworks: TensorFlow or PyTorch. - Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), and LLM evaluation strategies. - Experience working with REST APIs, data ingestion pipelines, and automation workflows. - Strong analytical thinking, problem-solving skills, and the ability to convert complex technical work into business-relevant insights. In addition, exposure to the chemical or energy industry, or prior experience in market research/analyst workflows, familiarity with frameworks such as OpenAI Agentic SDK, CrewAI, AutoGen, SmolAgent, etc., experience deploying ML/LLM solutions to production environments (Docker, CI/CD), hands-on experience with vector databases such as FAISS, Weaviate, Pinecone, or ChromaDB, experience with dashboarding tools and visualization libraries (e.g., Streamlit, Plotly, Dash, or Tableau), and exposure to cloud platforms (AWS, GCP, or Azure), including usage of GPU instances and model hosting services would be preferred. (Note: The above job description is based on the information provided in the job listing and may be subject to change or modification as per the company's requirements.),