GenAI Data Scientist

Qualifications & Skills

• Experience: professional experience in data science, machine learning, and AI, with specific experience in NLP, LLMs, and GenAI applications.

• Programming Languages: Strong proficiency in Python is mandatory, with experience in data science libraries like NumPy, Pandas, and Scikit-learn. Knowledge of SQL is also essential for data extraction and manipulation.
• AI/ML Frameworks: Expertise deep learning frameworks such as PyTorch and TensorFlow. Familiarity with Hugging Face transformers, NLTK, or SpaCy is also common.
• GenAI/NLP Specific Tools & Techniques:
• Experience with LLM frameworks like LangChain and LlamaIndex.
• Knowledge of building retrieval systems using vector databases (e.g., Pinecone, Weaviate, ChromaDB, FAISS).
• Proficiency in NLP techniques include embeddings, topic modeling, text classification, semantic search, and summarization.
• Cloud Platforms: Hands-on experience with major cloud computing services (AWS, Azure, GCP) for model deployment and leveraging services like Azure OpenAI, Google Vertex AI, or AWS Bedrock.
• Problem-Solving: Strong analytical, problem-solving, and statistical skills.