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Senior Data Scientist
2 weeks ago
Hello,
Hope you are doing good.
We are hiring.
Please revert me back with your updated resume at if you are interested.
1 Opening into Sr./ Lead Data Scientist
2 Openings into Data Scientist
Below are the details:
Job Location: B-44, TechSci Research Building, Sector 57, Noida
Profile: Sr. AI/ML Expert/ Data Scientist
Overview:
We are seeking a highly skilled and experienced AI/ML Expert to spearhead the design, development, and deployment of advanced artificial intelligence and machine learning models. This role requires a strategic thinker with a deep understanding of ML algorithms, model optimization, and production-level AI systems. You will guide cross-functional teams, mentor junior data scientists, and help shape the AI roadmap to drive innovation and business impact. This role involves statistical analysis, data modeling, and interpreting large sets of data.
An AI/ML expert with experience in creating AI models for data intelligence companies who specializes in developing and deploying artificial intelligence and machine learning solutions tailored for data-driven businesses. This expert should possess a strong background in data analysis, statistics, programming, and machine learning algorithms, enabling them to design innovative AI models that can extract valuable insights and patterns from vast amounts of data.
About the Role
The design and development of a cutting-edge application powered by large language models (LLMs). This tool will provide market analysis and generate high-quality, data-driven periodic insights. You will play a critical role 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 discussions.
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.
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.
