Data Scientist II

Primary Responsibilities

• Collaborate with business stakeholders to understand and translate their goals into AI and data science initiatives.
• Lead the development and implementation of LLM-based workflows and AI agents to drive automation, personalization, and intelligent decision-making.
• Develop strategies to optimize budget allocation and campaign performance using AI-driven approaches in scenarios with high cardinality and uncertainty.
• Conduct exploratory data analysis to uncover trends and insights from large, complex data sets.
• Identify, evaluate, and deploy use case-specific LLMs (e. g., OpenAI, Gemini, Claude) for summarization, retrieval, semantic search, tool use, and classification.
• Design and implement retrieval-augmented generation (RAG) pipelines and memory-augmented AI agents.
• Implement Groq or similar platforms for high-performance, low-latency inference and scalable AI deployment.
• Communicate complex analytical and AI-driven findings in a clear, actionable manner to non-technical stakeholders.
• Stay abreast of the latest advancements in AI, LLMs, and agentic systems.
• Build AI-powered proof of concepts (PoCs) leveraging foundation models, vector databases, and orchestration frameworks.
• Use SQL for data exploration, feature engineering, and prompt conditioning.
  

Required Skills

• Qualification in a quantitative field such as Computer Science, Artificial Intelligence, Statistics, Physics, or Mathematics.
• Excellent problem-solving skills and strategic thinking with a strong AI product mindset.
• Strong coding skills, particularly in Python.
• 4-6 years of relevant work experience in Data Science, with significant hands-on experience in LLM-based application development.
• Solid foundation in statistical analysis, experimentation, and hypothesis testing.
• Proficiency in Python and SQL.
• Preferred experience on the GCP platform.
• Proven experience with LLMs, including prompt engineering, fine-tuning, RAG, and evaluation.
• Experience identifying and scaling LLM-based use cases across business functions.
• Familiar with building AI agents using LangChain, LangGraph, and agentic orchestration frameworks.
• Experience with Groq or similar platforms for high-speed inference of LLMs.
• Experience with LangSmith (LLMOps) for debugging and monitoring LLM workflows.
• Hands-on experience with fine-tuning LLMs for domain-specific applications.
• Practical experience in classical machine learning techniques and working with large-scale datasets.
• Plus points for experience in AdTech or Meta Ads.
• Effective communication skills with the ability to convey technical concepts to non-technical audiences.