Senior Data Scientist – ML + GenAI / Agentic AI

About the Role

This is not a research-only or GenAI exploration role. We are looking for an experienced practitioner who has built and deployed AI/ML solutions in production, can work closely with product and engineering teams, and can confidently communicate technical concepts and architecture decisions to customers and non-technical stakeholders. Experience in the energy domain is highly preferred.

Key Responsibilities

• Design, develop, and deploy machine learning models for complex business problems.
• Apply traditional ML techniques including classification, regression, forecasting, anomaly detection, feature engineering, and model evaluation.
• Build and productionize LLM and Generative AI solutions.
• Develop Agentic AI workflows using technologies such as LangGraph, LangChain, MCP, tool calling, and agent orchestration.
• Design and implement production-grade RAG solutions, including vector databases, hybrid retrieval, reranking, and knowledge graphs.
• Work with AWS Bedrock or comparable enterprise GenAI cloud platforms.
• Analyze large and complex datasets using Python, SQL, and modern data platforms.
• Collaborate with product managers, engineers, customers, and cross-functional stakeholders.
• Communicate architecture decisions, model performance, trade-offs, and recommendations to both technical and non-technical audiences.
• Support production deployment, monitoring, evaluation, and continuous improvement of AI/ML systems.
• Mentor junior data scientists and contribute to technical best practices.

Required Skills

• 5–7 years of relevant Data Science / Machine Learning experience.
• Strong hands-on traditional ML/Data Science background.
• Strong Python and SQL skills.
• Experience with ML frameworks such as Scikit-learn, XGBoost, CatBoost, TensorFlow, or PyTorch.
• 12+ months of recent, continuous hands-on GenAI/LLM experience.
• Proven production deployment of AI/ML or GenAI systems.
• Hands-on Agentic AI production experience, including one or more of: LangGraph, LangChain, MCP, tool calling, agent orchestration.
• Strong RAG implementation experience, including vector databases, hybrid retrieval, reranking, and knowledge graphs.
• AWS Bedrock experience strongly preferred. Azure OpenAI or Google Vertex AI may be considered as alternate GenAI cloud experience.
• Experience with big data technologies such as Spark/Hadoop.
• Experience with Databricks and/or Snowflake is preferred.
• Experience with LLMOps, evaluation, monitoring, or AI evaluation tooling is a plus.
• Strong communication and client-facing/consulting skills.

Preferred Domain Experience

• Energy / Utilities / Oil & Gas
• Enterprise technology
• Consulting
• Large-scale enterprise products