Data Scientist; Mid-Senior

Role Overview

Alpha7X is hiring a mid-to-senior Data Scientist with a strong technical foundation and hands‑on experience building machine learning systems from the ground up. This role focuses on applied modeling, natural language systems, and agentic AI architectures used in production environments. We are not looking for candidates whose experience is limited to prompt engineering or thin wrappers around hosted APIs. The ideal candidate understands how models work, how to evaluate them, and how to integrate them into larger systems that reason over data, documents, and tools.

• Design, train, and evaluate machine learning models from first principles, including feature engineering, model selection, and validation
• Develop and maintain production‑quality Python code for data processing, training, and inference workflows
• Build natural language processing systems for document understanding, classification, extraction, and semantic search
• Design and implement Retrieval‑Augmented Generation (RAG) pipelines, including embedding strategies, retrieval evaluation, and grounding techniques
• Build and maintain agentic AI systems that coordinate models, tools, and multi‑step reasoning workflows
• Work with unstructured and semi‑structured data at scale
• Collaborate with engineering and infrastructure teams to deploy and operate models in production
• Analyze model performance, failure modes, and data drift, and drive iterative improvement

• Strong proficiency in Python for data science and machine learning
• Experience building ML models from scratch rather than relying exclusively on pre‑trained or hosted APIs
• Solid understanding of statistics, linear algebra, and core ML concepts
• Demonstrated experience with natural language processing workflows
• Hands‑on experience designing and implementing RAG systems
• Experience with agentic AI patterns and relevant tooling, such as Lang Chain, Llama Index, or similar orchestration frameworks;
  Vector databases and embedding pipelines;
  Tool‑calling, planner‑executor, or multi‑agent coordination patterns
• Experience working with real production data and systems

• Experience with graph databases, particularly Neo4j
• Familiarity with graph‑based modeling, knowledge graphs, or Graph‑RAG approaches
• Experience deploying ML systems in cloud environments such as AWS or Azure
• Exposure to MLOps, containerization, or CI/CD pipelines
• Experience with document‑heavy or compliance‑oriented domains
• Experience with cloud such as Azure