Senior Data Scientist

🚀 Hiring: Senior Data Scientist – GenAI / RAG

📍 Location: Houston, TX

💼 Job Type: Full-Time

💰 Experience: 5–15 years

Work Authorization: US Citizen, Green Card, or H-1B Transfer

🏢 Interview: Must be willing to attend an in-person interview in Santa Clara, CA

We are looking for a Senior Data Scientist who combines a strong foundation in traditional Machine Learning/Data Science with hands-on, recent experience building and deploying Generative AI, LLM, RAG, and Agentic AI solutions in production.

This is not a research-only, academic, or POC-focused role. We are looking for someone who has shipped real-world AI/ML systems, can work closely with product and engineering teams, and is comfortable interacting with customers and non-technical stakeholders.

🔥 What You'll Work On

• Develop and deploy machine learning models to solve complex business problems

• Build predictive models for classification, regression, forecasting, anomaly detection, and other use cases

• Design and implement production-grade RAG architectures

• Develop agentic AI workflows using LangGraph, LangChain, MCP, tool calling, and agent orchestration

• Work with vector databases, hybrid retrieval, reranking, and knowledge graphs

• Leverage AWS Bedrock and other enterprise GenAI platforms

• Analyze large and complex datasets to identify actionable insights

• Collaborate with product managers, engineers, and enterprise customers

• Translate technical architecture and ML/AI concepts into clear business recommendations

• Deploy, monitor, evaluate, and continuously improve AI/ML systems

• Mentor junior data scientists and contribute to a collaborative technical environment

✅ Must-Have Qualifications

• 5–15 years of relevant Data Science / Machine Learning experience

• Strong traditional ML/DS foundation including:

• Classification
• Regression
• Forecasting
• Anomaly detection
• Feature engineering
• Model evaluation
• • 12+ months of recent, continuous hands-on GenAI/LLM experience
• • Production experience with Agentic AI, including:
• LangGraph

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• LangChain
• MCP
• Tool calling
• Agent orchestration
• • Deep hands-on RAG implementation experience:
• Vector databases
• Hybrid retrieval
• Reranking
• Knowledge graphs
• • Hands-on experience with AWS Bedrock
• Azure OpenAI or Google Vertex AI experience is also considered
• • Strong Python skills; R is a plus
• • Experience with ML frameworks such as Scikit-learn, PyTorch, or TensorFlow
• • Production deployment experience — candidates should have shipped AI/ML systems into live environments
• • Strong SQL and experience working with large datasets
• • Experience with Spark, Hadoop, or similar big-data technologies
• • Strong communication and stakeholder-management skills

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• • Ability to explain technical architecture, trade-offs, and recommendations to non-technical audiences
• • Customer-facing or consulting experience is highly preferred
• • Bachelor's/Master's/Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field

⭐ Nice to Have

• Energy, utilities, oil & gas, or natural resources industry experience

• Databricks

• Snowflake

• XGBoost / CatBoost

• LLMOps and AI evaluation tooling

• Experience building enterprise AI products

🎯 Who Will Succeed in This Role?

The ideal candidate is a hands-on Data Scientist who has evolved into GenAI/LLM engineering, rather than someone who has only recently started experimenting with GenAI.

You should be comfortable moving between traditional ML → RAG → agentic workflows → production deployment → customer conversations.

If you have built and deployed real-world GenAI systems and enjoy solving complex enterprise problems, we'd love to hear from you.