TechSci Research - Lead Data Scientist - LLM Models

About The Role

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 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 closedand 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.
  

Preferred

• Familiarity with the chemical or energy industry, or prior experience in market research/analyst workflows.
• Exposure to 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).
• Exposure to cloud platforms (AWS, GCP, or Azure), including usage of GPU instances and model hosting services.