Senior Data Scientist-Gen AI/RAG

Job Description

Job Description

Job Description

Role : Senior Data Scientist-Gen AI/RAG

Domain : Information technology (IT)

Job Type: Full Time

Location : USA - Houston

Experience: 7 - 15 Years

Job Summary:

Looking for a Senior Data Scientist with a strong traditional ML/DS background and hands-on experience in GenAI (LLMs, RAGs, Agentic workflows), we need someone with practical implementation experience and the ability to interact confidently with customers. The selected candidate will leverage advanced analytical techniques and machine learning methods to derive actionable insights from complex datasets.

Key Responsibilities:

• Develop and implement machine learning algorithms to solve complex business problems. 
• Analyze large datasets to generate insights and inform decision-making processes. 
• Collaborate with product managers and engineers to integrate data science solutions into enterprise products.
• Communicate findings and recommendations effectively to technical and non-technical stakeholders.
• Stay current with the latest advancements in data science and machine learning technologies.

Skills and Tools Required:

• Strong proficiency in programming languages such as Python or R.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid understanding of statistical analysis techniques and data modeling.
• Proficiency in data visualization tools (e.g., Tableau, Power BI).
• Familiarity with big data technologies (e.g., Hadoop, Spark).
• Ability to work with databases and query large datasets using SQL.
• Strong problem-solving skills and the ability to think critically.

Must Haves:

• A 5-to-7-years of Relevant experience is needed. 
• Hands-on Agentic AI production experience — LangGraph, LangChain, MCP, tool calling.
• RAG implementation depth — vector databases, hybrid retrieval, reranking, knowledge graphs.
• AWS Bedrock experience (Azure OpenAI or Vertex AI only acceptable as an alternate, not a substitute for GenAI cloud exposure generally).
• Strong traditional ML/DS background — classification, regression, forecasting, anomaly detection, feature engineering, model evaluation.
• 12+ months continuous, recent hands-on GenAI/LLM work.
• Production deployment experience